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Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification
Abstract
This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.
... The handcrafted features sometimes fail to capture essential information present in data and limit the learning capability of machine learning models and reduce the performance. In the past, researchers have used classifiers such as weighted conditional random fields (CRF) [9], support vector machines (SVM) [1,7,11,25,31,33], mixtureof-experts [18], and clustering [43]. Deep Neural Networks have achieved exceptional performance for heartbeat classification. ...
... Fig. 7 and Fig. 8 illustrates the architecture of CNN and ResNet. The models were trained with L layers, where L ∈ [1,9]. Here, a ResNet with 5 layers is represented as R5. ...
... Comparison with Previous Methods: The comparison of PIPxResNet with previous techniques is provided in Table 7. The previous methods used RR interval [8], morphological features [9], wavelet based features [10,17,26,41,44], discrete cosine transform (DCT) [25], stockwell transform (ST) [7], higher order statistical (HOS) features [23], Hermite polynomial based features (HBF) [20], mixture of features [6], and complex heartbeat representations from temporal vectorcardiogram [11]. The extracted feature redundancy is handled through dimensionality reduction techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA) [26]. ...
Early stage heartbeat classification using the electrocardiogram signals can prevent cardiovascular diseases that causes millions of deaths annually around the world. In the past, researchers have used deep neural networks to achieve significant performance for heartbeat classification but their black-box nature and prediction rationale limits real-world deployment. We propose a Penalty Induced Prototype based eXplainable Residual Neural Network (PIPxResNet) that addresses the black-box nature of deep neural networks. PIPxResNet encodes the temporal variations of heartbeats by employing pretrained residual neural network following the concept of task transfer learning. The algorithm further extracts prototypes that are most representative of the training dataset that explain model predictions to general physicians, making them clinically relevant. The prototypes of a particular class having close resemblance to other class prototypes are penalised and their contribution towards corresponding class is reduced. In addition, the classification performance is improved by synthesising regular and irregular heartbeats using a deep convolution conditional generative adversarial network. The proposed method can easily be adopted to other domains that requires explanations for the classification tasks. The PIPxResNet performs at par with existing state-of-the-art algorithms without compromising individual class performance when tested on four publicly available annotated datasets. The proposed model is capable to perform automated screening and provide medical attention by simulating a clinical decision support system for general physicians.
... However, the models with this scheme are not good for classifying data from patients it has never seen before. To make the model comply with the new patient, the expert must manually annotate a portion of heartbeats from this new patient and re-train the model with the additional data [36]. ...
... Forty-four records from the MIT-BIH dataset were split into two sets, namely DS1 and DS2, with DS1 used for training and DS2 for testing. Although this scheme was believed to be the most realistic scenario [9], the challenge of this task is much higher than the intra-patient scheme [9], [36]. Therefore, it is not uncommon for a model with high accuracy in the former scheme to get a lower score when it is applied to this scheme. ...
Arrhythmia is an irregular heartbeat that may cause serious problems such as cardiac arrest and heart failure if left untreated. A dozen of studies have been conducted to make an automated arrhythmia detector. The classification approach uses a simple rule-based model, traditional machine learning, to a modern deep-learning technique. However, comparing an arrhythmia classifier performance is not an easy task. There are several different datasets, classification standards, data splitting schemes, and metrics. To assess the real performance of the developed models, it is important to train and evaluate the model in a standardized method such as the result score can become standard too. In this study, a set of CNN models from Acharya were re-implemented by retraining and re-evaluating it in a more standardized method. The model uses a raw ECG waveform with 260 samples around the QRS peaks and classifies it into five arrhythmia classes. The experiment was conducted using three configurations, using both intra-patient and inter-patient schemes. The experimental results show good performance for the intra-patient scheme but not for the inter-patient. There is a reduction of sensitivity and precision in the intra-patient scheme using a standardized method in this study compared to the original paper. This result indicates biased results caused by the oversampled test data in the original paper. In addition to the intrapatient result, the inter-patient result is also provided for a standardized comparison to other works in the future. © IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License
... Other feature extraction techniques include wavelet packet entropy [9], linear prediction [10], abstract [11], and auto-encoder [12]. There have also been various feature selection and dimensionality reduction methods such as F-score [13], mutual information criterion [15], genetic algorithm [14], principal component analysis(PCA) [10], and neighbourhood component analysis (NCA) [54]. ...
... Cardiac arrhythmia classification is performed by using various machine learning and deep learning algorithms such as support vector machine (SVM) [53], random forest (RF) [16], decision tree (DT) [17], K-nearest neighbor (KNN) [54], linear discriminant (LD) [18], Gaussian mixture model (GMM) [19], Multilayer perceptions (MLPs) [20], fuzzy neural networks [21], radial basis networks [22], convolutional neural network (CNN) [23,24], optimum-path forest [25], conditional random fields [15] and ensemble of classifiers [26]. The higher parameters of the classifiers were optimized by various algorithms such as grid search or genetic algorithms [27,28]. ...
Cardiac Arrhythmias is defined as the sudden and unexpected occurrence of a rhythm in the heart. Arrhythmia detection is prevalent in developing countries that require systems-based screening solutions for diagnosis. A new rhythm-based approach is proposed to screen the patients with cardiac arrhythmia at the primary level. This method eliminates multiple tests, enabling faster and more accurate diagnosis. This method first segments the electrocardiogram signals to create a complete picture of the single heartbeat. Then the Fourier–Bessel series expansion (FBSE) is used to transform the sequences of each heartbeat into more meaningful ones that can characterize the structural integrity of arrhythmia. The FBSE sequence series are trained using the Jaya optimized ensemble random subspace KNN (JO-ERSKNN) model with 10-fold cross-validation for classifying five types of cardiac arrhythmia beats. We have achieved an accuracy of 99.49%, a sensitivity of 95.43%, and specificity of 99.48%. The results demonstrate that the proposed algorithm can detect differences in the five types of cardiac arrhythmia signals. It can also be utilized as a screening tool for detecting arrhythmia and can be made compatible with various wearable devices.
... To relieve the cardiologists from such laborious task computer-assisted methods are needed. In the past decade, plenty of works [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] has been reported in the field of automatic heartbeat classification. The reported work can be divided into 2 categories, named "intra-patient" also known as "classbased" and "inter-patient" also known as "subject-based" classification strategy. ...
... In this study, MIT-BIH arrhythmia database [20][21][22] is used. It is widely employed as a standard data for ECG signal de-noising [23], ECG compression [24,25] and ECG beat classification [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] There is an annotation file containing R-peak locations and type of heartbeats associated with each recording, which helps in heartbeat segmentation and employed as ground truth for algorithm assessment respectively. Four records (102, 104, 107, and 217) having pace beats are excluded from the experiment. ...
Heartbeat classification algorithm for detection of different arrhythmia
... Various types of techniques have been investigated and analyzed for this purpose and other purposes such as decision trees [6], random forest (RF) [7,8], k-nearest neighbors (KNN) [9,10], hidden Markov models [11,12], hyper-box classifiers [13], optimum-path forest [14], conditional random fields [15], besides other methods such as [16][17][18][19][20][21]. ...
... These techniques provide promising results, but they cause an interesting computational cost. Authors in [15] suggested the use of weighted Conditional Random Fields for the classification of arrhythmias and compared it with support vector machine (SVM) and LDs. The analysis revealed that the introduced approach gets promising results for the minority arrhythmical classes (SVEB e VEB). ...
The new advances in multiple types of devices and machine learning models provide opportunities for practical automatic computer-aided diagnosis (CAD) systems for ECG classification methods to be practicable in an actual clinical environment. This imposes the requirements for the ECG arrhythmia classification methods that are inter-patient. We aim in this paper to design and investigate an automatic classification system using a new comprehensive ECG database inter-patient paradigm separation to improve the minority arrhythmical classes detection without performing any features extraction. We investigated four supervised machine learning models: support vector machine (SVM), k-nearest neighbors (KNN), Random Forest (RF), and the ensemble of these three methods. We test the performance of these techniques in classifying: Normal beat (NOR), Left Bundle Branch Block Beat (LBBB), Right Bundle Branch Block Beat (RBBB), Premature Atrial Contraction (PAC), and Premature Ventricular Contraction (PVC), using inter-patient real ECG records from MIT-DB after segmentation and normalization of the data, and measuring four metrics: accuracy, precision, recall, and f1-score. The experimental results emphasized that with applying no complicated data pre-processing or feature engineering methods, the SVM classifier outperforms the other methods using our proposed inter-patient paradigm, in terms of all metrics used in experiments, achieving an accuracy of 0.83 and in terms of computational cost, which remains a very important factor in implementing classification models for ECG arrhythmia. This method is more realistic in a clinical environment, where varieties of ECG signals are collected from different patients.
... ECG signals possess the following categories of noise: powerline interference, electrode-skin interference, radio frequency surgical noise, and baseline drift due to respiration [28]. Various noise removal techniques reported in the literature are bandpass filtering [29][30][31][32][33][34][35][36], average and median filter [37][38][39][40][41], wavelet transform [40,[42][43][44]. Table 1 shows the list of commonly used ECG-based preprocessing methods found in the literature. ...
... Others classifiers that can be used for the classification of arrhythmia are non-singleton fuzzy logic classifier [82], modular neural network [83] and [42], multi-layered perceptron [40], fuzzy clustering NN [57], naive Bayes classifier [36], neuro SVM-KNN fusion classifier [47], convolutional neural network [29,84,85] linear discriminant classifier [81,86], particle swarm optimization classifier [32,87] and conditional random fields classifier [39]. ...
Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that provide electrical and hemodynamic information of the heart, respectively. This information is advantageous in the diagnosis of various cardiac abnormalities. Arrhythmia is the most common cardiovascular disease, manifested as single or multiple irregular heartbeats. However, due to the continuous manual observation, it becomes troublesome for experts sometimes to identify the paroxysmal nature of arrhythmia correctly. Moreover, due to advancements in technology, there is an inclination towards wearable sensors which monitor such patients continuously. Thus, there is a need for automatic detection techniques for the identification of arrhythmia. In the presented work, ECG and PPG-based state-of-the-art methods have been described, including preprocessing, feature extraction, and classification techniques for the detection of various arrhythmias. Additionally, this review exhibits various wearable sensors used in the literature and public databases available for the evaluation of results. The study also highlights the limitations of the current techniques and pragmatic solutions to improvise the ongoing effort.
... Data segmentation is used to isolate heartbeats from the whole ECG recording. Once a time segment including the heartbeat is available, time domain [11][12][13][14][15][16] or frequency domain [13,[16][17][18] or morphological [11][12][13]15,16] or statistical [13,19] or neural features [20] are extracted. ...
... Data segmentation is used to isolate heartbeats from the whole ECG recording. Once a time segment including the heartbeat is available, time domain [11][12][13][14][15][16] or frequency domain [13,[16][17][18] or morphological [11][12][13]15,16] or statistical [13,19] or neural features [20] are extracted. ...
This paper puts forward a 1-D convolutional neural network (CNN) that exploits a novel analysis of the correlation between the two leads of the noisy electrocardiogram (ECG) to classify heartbeats. The proposed method is one-dimensional, enabling complex structures while maintaining a reasonable computational complexity. It is based on the combination of elementary handcrafted time domain features, frequency domain features through spectrograms and the use of autoregressive modeling. On the MIT-BIH database, a 95.52% overall accuracy is obtained by classifying 15 types, whereas a 95.70% overall accuracy is reached when classifying 7 types from the INCART database.
... Many research groups have been proposed for automatic detection and classification of various types of arrhythmias [7][8][9][10][11][12][13]. ...
Detection of Atrial fibrillation (AF) is more complex as compared to other cardiac diseases. It requires lengthy ECG signals and more time for visual inspection and analysis by the physicians. Automatic detection of AF using an expert system is essential for the investigation of ECG signals. In this study, the Physionet challenge 2017 dataset is used for the detection and classification of AF versus other signals. In this paper, ECG signals are segmented into a sample size of 250 samples for the detection of Wavelet Packet Decomposition (WPD) and approximate entropy (ApEn) features for classification. In addition to WPD and ApEn, statistical features were derived from ECG signals. The Principal Component Analysis (PCA) has been used to reduce the dimensionality of the features based on the rank. Ensemble classifiers such as AdaBoost, XGBoost and Random Forest (RF) are used for classification. The accuracy of 62.91%, 70.33% and 89% for the AdaBoost, XGBoost and Random Forest respectively. We found RF classifier is suitable for classifying AF, normal rhythm and other non-AF related abnormal heart rhythms.
... In [14], a new heartbeat recognition method based on a principal component analysis network (PCANet) and linear support vector machine (SVM) is proposed, which reduces the noise disturbance of heartbeat effectively. What is more, such as hyper-frame classifier [15], self-organizing map [16], conditional random field [17] and integration method [18], all the above methods are necessary to establish accurate system models, and most of them are based on artificial feature extraction, which is cumbersome. For the DL classification algorithms, in [19], multiscale Laplacian graph kernel features (MLGK) are proposed, which enriches the characteristics of the extracted ECG signals. ...
Electrocardiogram (ECG) signal plays a key role in the diagnosis of arrhythmia, which will pose a great threat to human health. As an effective feature extraction method, deep learning has shown excellent results in processing ECG signals. However, most of these methods neglect the cooperation between the multi-lead ECG series correlation and intra-series temporal patterns. In this work, a multi-domain collaborative analysis and decision approach is proposed, which makes the classification and diagnosis of arrhythmia more accurate. With this decision, we can realize the transition from the spatial domain to the spectral domain, and from the time domain to the frequency domain, and make it possible that ECG signals can be more clearly detected by convolution and sequential learning modules. Moreover, instead of the prior method, the self-attention mechanism is used to learn the relation matrix between the sequences automatically in this paper. We conduct extensive experiments on eight advanced models in the same field to demonstrate the effectiveness of our method.
... In the method of manual feature extraction, the manually extracted features [3,6,7,8,9,10,11,12,13,14] mainly include RR interval, short-time Fourier transform, morphological features, empirical modal decomposition, higherorder statistics, and entropy metric. en machine learning classifiers are adopted to classify the extracted features, including the weighted linear discriminator [15,16,17,18], support vector machine [4,19,20,21,22,23], multilayer perceptron [24], and convolutional neural network [25,26,27,28]. Chazal et al. [29] extracted five groups of features, including R-R, HOS, wavelet, morphology, and LBP, from ECG signals and used these features to classify ECG signals by linear classifier. ...
Automated electrocardiogram classification techniques play an important role in assisting physicians in diagnosing arrhythmia. Among these, the automatic classification of single-lead heartbeats has received wider attention due to the urgent need for portable ECG monitoring devices. Although many heartbeat classification studies performed well in intrapatient assessment, they do not perform as well in interpatient assessment. In particular, for supraventricular ectopic heartbeats (S), most models do not classify them well. To solve these challenges, this article provides an automated arrhythmia classification algorithm. There are three key components of the algorithm. First, a new heartbeat segmentation method is used, which improves the algorithm’s capacity to classify S substantially. Second, to overcome the problems created by data imbalance, a combination of traditional sampling and focal loss is applied. Finally, using the interpatient evaluation paradigm, a deep convolutional neural network ensemble classifier is built to perform classification validation. The experimental results show that the overall accuracy of the method is 91.89%, the sensitivity is 85.37%, the positive productivity is 59.51%, and the specificity is 93.15%. In particular, for the supraventricular ectopic heartbeat(s), the method achieved a sensitivity of 80.23%, a positivity of 49.40%, and a specificity of 96.85%, exceeding most existing studies. Even without any manually extracted features or heartbeat preprocessing, the technique achieved high classification performance in the interpatient assessment paradigm.
... The authors reported an overall accuracy of 86.4% in the patient-specific evaluation. In another study [27], a weighted variant of the conditional random fields classifier (CRF) was used with L1 regularization and achieved an accuracy of 85%. Khorrmi et al. conducted a comparative study of feature extraction and classification methods. ...
Objective:
Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performance. Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification.
Results:
Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs). Significance: As a pioneer application, the results show that compact and shallow 1D Self-ONNs with the feature injection can surpass all state-of-the-art deep models with a significant margin and with minimal computational complexity.
Conclusion:
This study has demonstrated that using a compact and superior network model, a global ECG classification can still be achieved with an elegant performance level even when no patient-specific information is used.
... The work related to the automatic classification of ECG signals is summarized in Table 5. Many machine learning methods have been proposed for the classification of five arrhythmias [40][41][42][43][44][45][46][47][48]. Previous studies tend to conduct features manually, which is time-consuming. ...
Arrhythmia is a significant cause of death, and it is essential to analyze the electrocardiogram (ECG) signals as this is usually used to diagnose arrhythmia. However, the traditional time series classification methods based on ECG ignore the nonlinearity, temporality, or other characteristics inside these signals. This paper proposes an electrocardiogram classification method that encodes one-dimensional ECG signals into the three-channel images, named ECG classification based on Mix Time-series Imaging (EC-MTSI). Specifically, this hybrid transformation method combines Gramian angular field (GAF), recurrent plot (RP), and tiling, preserving the original ECG time series’ time dependence and correlation. We use a variety of neural networks to extract features and perform feature fusion and classification. This retains sufficient details while emphasizing local information. To demonstrate the effectiveness of the EC-MTSI, we conduct abundant experiments in a commonly-used dataset. In our experiments, the general accuracy reached 93.23%, and the accuracy of identifying high-risk arrhythmias of ventricular beats and supraventricular beats alone are as high as 97.4% and 96.3%, respectively. The results reveal that the proposed method significantly outperforms the existing approaches.
... Among the classifiers, researchers have used simple classifiers like linear discriminant classifier [22], K-nearest neighbor [23], artificial neural networks [24], hidden Markov model [25], conditional random field (CRF) [26], etc. A number of works are found based on the kernel classification methods. ...
Arrhythmia is the most fatal for human being among all cardiovascular diseases. Early detection of arrhythmia beats, from long term ECG record, is helpful to start treatment and saving life of patients. In this work, we presented a patient-adaptive scheme to discriminate normal and three classes of arrhythmia beats from ECG signal. Instead of conventional features, the proposed method uses a kernel based modeling technique of the ECG beats and the model coefficients are used as the features to characterize different types of beats. In this semi automatic scheme, a global training set is combined with a local learning set to form a patient adaptive training set to develop a patient specific classifier model. The results are validated on MIT-BIH arrhythmia database and the performance of the proposed technique is validated by three classifiers namely, support vector machine (SVM), vector valued regularized kernel function approximation (VVRKFA) technique and k-nearest neighbour (KNN) classifiers. Experimental results indicate that the proposed patient adaptive classification scheme increases the global accuracy by 12 to 16% than that of the accuracy obtained without using patient specific beats to global training set. The highest average accuracy obtained using this method is 96.63%, which is comparable and even better than most of the works available in the literature.
... The authors reported an overall accuracy of 86.4% in the patient-specific evaluation. In another study [26], a weighted variant of the conditional random fields classifier (CRF) was used with L1 regularization and achieved an accuracy of 85%. Khorrmi et al. conducted a comparative study of feature extraction and classification methods. ...
Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of both normal and arrhythmic ECG patterns among patients. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performances. This study proposes a novel approach to narrow this gap and propose a real-time solution with shallow and compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs).
... Among the classifiers, researchers have used simple classifiers like linear discriminant classifier [22], K-nearest neighbor [23], artificial neural networks [24], hidden Markov model [25], conditional random field (CRF) [26], etc. A number of works are found based on the kernel classification methods. ...
... The features extraction step is quite challenging compared to the classification step as it is important to extract the accurate and unique QRS complex as features. Feature removal techniques incorporate wave shape functions (deChazal, P., 2004), Hermite functions (Lagerholm, M., 2000), wavelet-based features (Ince, T., 2009), and statistical features (De Lannoy, G., 2012). Techniques to organize certain extracted features incorporate decision trees (Rodríguez, 2005), SVM (Rodríguez, 2005), KNN Jung, W.-H., 2017), linear discriminants (Jiang, W., 2007) and ANN (Jiang, W., 2007). ...
The heart disease detection and classification using the cost-effective tool electrocardiogram (ECG) becomes interesting research considering smart healthcare applications. Automation, accuracy, and robustness are vital demands for an ECG-based heart disease prediction system. Deep learning brings automation to the applications like Computer-Aided Diagnosis (CAD) systems with accuracy improvement compromising robustness. We propose the novel ECG-based heart disease prediction system using the hybrid mechanism to satisfy the automation, accuracy, and robustness requirements. We design the model via the steps of pre-processing, hybrid features formation, and classification. The ECG pre-processing is aiming at suppressing the baseline and powerline interference without loss of heartbeats. We propose a hybrid mechanism that consists of handcrafted and automatic Convolutional Neural Network (CNN) lightweight features for efficient classification. The hybrid feature vector is fed to the deep learning classifier Long Term Short Memory (LSTM) sequentially to predict the disease. The simulation results show that the proposed model reduces the diagnosis errors and time compare to state-of-art methods.
... Temporal data mining can be used to model the temporal relationship between diagnosis, treatment, and outcome, chronologically from the EHR data using techniques such as the hidden Markov Model [83] and the conditional random field. [84] The temporal association can be simplified as an antecedent followed by an outcome with a given time difference. The major constraint of this strategy is the requirement of predefined clinical variables and outcomes, which are difficult to generalize for a given treatment. ...
The healthcare industry generates a large amount of data, driven by record keeping, patient care, compliance and regulatory requirements. The digitization of the information is called “Big Data”, which is capable of supporting a wide range of medical and healthcare functions. Big Data Analytics (BDA) in healthcare is evolving into a promising field for providing insight from very large data sets and has the potential to improve the quality of healthcare delivery with a reduced cost. BDA has a significant impact on healthcare delivery and holds favourable support in a wide range of medical and healthcare applications that includes clinical decision support, disease surveillance, and population health management. BDA has brought in transformation in healthcare and enabled researchers and practitioners with tools to utilize data generated by healthcare systems globally. BDA also aids in preventing adverse events via early detection and diagnosis, leading to safer cost-effective procedures. In the interest of comprehending and analysing the complex biomedical data now accessible, BDA has become essential for the modeling, validating, and interpreting medical diagnosis through the broad spectrum of bioinformatics, medical imaging techniques, and precision medicine.
... For the domain adaptation task: DS1-DS2, the ARDB is divided into two datasets: DS1 and DS2 by convention [41] . This partition ensures that no heartbeats in the DS2 come from the same subject in the DS1, known as the inter-subject paradigm [42]. The labeled dataset DS1 is used as the source domain, and the unlabeled dataset DS2 is used as the target domain to verify the effectiveness of the USAFFN. ...
An electrocardiogram (ECG) consists of complex P-QRS-T waves. Detecting long-term ECG recordings is time-consuming and error-prone for cardiologists. Deep neural networks (DNNs) can learn deep representations and empower automatic arrhythmia detection. However, when applying DNNs in practice, they usually suffer from domain shift that exits between the training data and testing data. Such shift can be caused by the high variability contained in ECG signals between patients and internal-variability of heartbeats for same patients, leading to degrading performance and impeding generalization of DNNs. To tackle this problem, we propose an unsupervised semantic-aware adaptive feature fusion network (USAFFN) to reduce such shift by alleviating the semantic distribution discrepancy between the feature spaces of two domains. Furthermore, an ECG contains rich information from different angles (beat, rhythm, and frequency levels), which is essential for arrhythmia detection. Therefore, a multi-perspective adaptive feature fusion (MPAFF) module is introduced to extract informative ECG representations. The experimental results show that the detection performance of our approach is highly competitive with the upper bound of alternative methods on the ARDB, and the generalization is confirmed on the INCART and LTDB.
... Most of the work in this field is done on a standard MIT-BIH Arrhythmia dataset [23] using different machine learning classification algorithms. Achary et al. [1] classified five different kinds of heartbeats in an ECG record using the MIT-BIH Arrhythmia data set and a nine-layer CNN. de Lannoy [8] proposed a methodology for the classification of heartbeats from an ECG record. A weighted variant of conditional random field classifier using MIT-BIH Arrhythmia data set from physionet was used. ...
Heart disease patients are continuously increasing. The patients face the problem of a delayed diagnosis as the subjects do not undergo routine tests and consult a doctor only after severe symptoms. Most medical expert systems are designed to aid the doctors in making wise decisions and only such data sets exist in the literature. We attack the problem of an early-stage diagnosis that can be done at the home by the subject himself on a routine basis, using a low cost and compact ECG sensor. Machine learning tools nowadays have become important for data processing and assistance in various fields including medicine. Attributed to an absence of data, we first developed our ECG dataset by collecting ECG signal data from 300 persons including 53 cardiac patients and 247 healthy persons, using a low-cost and compact ECG sensor. To detect the heart diseases from this data, classical methods (Random forest and Gradient boosting) and state of the art Deep Learning models (1D Convolution Neural Net) were used. A problem with machine learning in the specific context is a severe data imbalance, for which oversampling of minority data was used. Since the sensor is a low cost, noise can get added up. Hence, voting across multiple time windows is done to improve the results. After a healthy comparison between all classification methods with different techniques based on their test accuracy, 1D CNN with oversampling and using voting strategy comes out as the best classifiers with a 93% test accuracy.
... These features fed to the SVM classifier and obtained an accuracy of 95.7%. de Lannoy et al. [124] implemented a Wavelet and Independent Component Analysis to extract features and fed to the SVM classifier and obtained an accuracy of 82.47%. Sharma et al. [125] segmented multi-lead electrocardiogram (ECG) signal was decomposed into separate sub-bands using the stationary wavelet transform. ...
... ICA and discrete wavelet transform (DWT) are utilized for morphological features extraction while the dynamic features of the ECG are calculated by RR intervals. The dynamic and temporal features directly rely upon the characteristics of the ECG where three important issues to judge onset point of the wave, the minimum and maximum points of the peaks and the offset point [5]. Non fiducial features are directly obtained by fiducial features, also from the ECG signal segmentation nonfiducial features are derived. ...
... Regarding the learning and classification models, it is possible to separate them into two major approaches with several specific algorithms. The first, traditional machine learning, has as examples: k-nearest neighbor [10], support vector machine (SVM) [6], Multi-Layer Perceptron (MLP) [23], conditional random fields [24], evolutionary neural system [12], random forest (RF) [19] e Linear Discriminant Analysis (LDA) [25]. The second, based on deep learning, replaces in most applications, the need for manual extraction of resources, abstracting the expression and generalization of nonlinear characteristics of the input into a single structure [26]. ...
Atrial fibrillation (AF) is the most common cardiac anomaly and one that potentially threatens human life. Due to its relation to a variation in cardiac rhythm during indeterminate periods, long-term observations are necessary for its diagnosis. With the increase in data volume, fatigue and the complexity of long-term features make analysis an increasingly impractical process. Most medical diagnostic aid systems based on machine learning, are designed to automatically detect, classify or predict certain behaviors. In this work, using the PhysioNet MIT-BIH Atrial Fibrillation database, a system based on MLP artificial neural network is proposed to differentiate, between AF and non-AF, segments and ECG’s features, obtaining average accuracy of 80.67% in test set, for the 10-fold cross-validation method. As a highlight, the extraction of jitter and shimmer parameters from ECG windows is presented to compose the network input sets, indicating a slight improvement in the model’s performance. Added to these, Shannon’s and logarithmic energy entropies are determined, also indicating an improvement in performance related to the use of fewer features.
... Among them, the two steps of feature extraction and classification are the most critical in the classification process, which are deeply studied by researchers. Furthermore, researches used numerous features to describe the ECG heartbeats, Hermite functions [13], morphological features [14,15], wavelet features [16,17], high-order statistical features [18,19], QRS amplitude vector [20], QRS complex wave area [21], and heartbeat intervals [22][23][24]. Over the past few decades, numerous algorithms have been developed to distinguish different types of arrhythmias, including linear classifier [25][26][27], decision tree [28,29], k-nearest neighbor [30][31][32], support vector machine [33,34], random forest [35,36], and ensemble classifier [37][38][39][40][41], etc. ...
Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, the classification strategy of the multifeature combination and Stacking-DWKNN algorithm is proposed in this paper. The method consists of four modules. In the preprocessing module, the signal is denoised and segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS length, T length, PR interval, ST segment, QT interval, RR interval, R amplitude, and T amplitude. Subsequently, the features are combined and normalized, and the effect of different feature combinations on heartbeat classification is analyzed to select the optimal feature combination. Finally, the four types of normal and abnormal heartbeats were identified using the Stacking-DWKNN algorithm. This method is performed on the MIT-BIH arrhythmia database. The result shows a sensitivity of 89.42% and a positive predictive value of 94.90% of S-type beats and a sensitivity of 97.21% and a positive predictive value of 97.07% of V-type beats. The obtained average accuracy is 99.01%. Compared to other models with the same features, this method can improve accuracy and has a higher positive predictive value and sensitivity, which is important for clinical decision-making.
... ICA and discrete wavelet transform (DWT) are utilized for morphological features extraction while the dynamic features of the ECG are calculated by RR intervals. The dynamic and temporal features directly rely upon the characteristics of the ECG where three important issues to judge onset point of the wave, the minimum and maximum points of the peaks and the offset point [5]. Non fiducial features are directly obtained by fiducial features, also from the ECG signal segmentation nonfiducial features are derived. ...
The ECG (Electrocardiogram) is the common reliable and easiest to utilize tool for diagnosis of cardiac arrhythmias. Manually diagnosing the arrhythmia beats is very hectic, as the ECG signals are non-linear and produce long records for analysis. It is very difficult for specialists to evaluate time domain features of minute variations, such as lines, intensity & intervals of ECG Signals in pure human judgments. This manuscript discusses an automatic approach to machine learning and the outcome of the initial algorithm identification of five separate heart rhythms. Support Vector Networks (SVN) is utilized to remove the features and besides that Independent Component Analysis ( ICA) is the technique utilized to provide reduction of dimensionality. The kernel support vector machine function works for the tenfold classification and the ECG Signal cross validation. The concept of variance analysis is utilized to select significant features, and accuracy reliability is measured by the assist of Cohen’s kappa statistics. The publicly available MIT-BIH database on arrhythmias is utilized to analyze various types of arrhythmias. This is a massive ECG data collection of various types of records and it includes five separate groups of classification arrhythmia such as SupraVEB, Non-ectopic, VEB, Unknown Beat(Ubeat) and Fusion beat(Fbeat). This methodology will produce an efficient tool to check a person’s cardiac health which will produce a smart, automated technology for the specialist and paramedics to deal with heart arrhythmia.
Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed. The multi-branch network with different receptive fields is used to extract the multi-spatial deep features of heartbeats. The Channel Attention Module (CAM) and Bidirectional Long Short-Term Memory neural network (BLSTM) module are used to filter redundant ECG features. CAM and BLSTM are beneficial for distinguishing different categories of heartbeats. In the experiments, a four-fold cross-validation technique is used to improve the generalization capability of the network, and it shows good performance on the testing set. This method divides heartbeats into five categories according to the American Advancement of Medical Instrumentation (AAMI) criteria, which is validated in the MIT-BIH arrhythmia database. The sensitivity of this method to Ventricular Ectopic Beat (VEB) is 98.5% and the F1 score is 98.2%. The precision of the Supraventricular Ectopic Beat (SVEB) is 91.1%, and the corresponding F1 score is 90.8%. The proposed method has high classification performance and a lightweight feature. In a word, it has broad application prospects in clinical medicine and health testing.
Myocardial Infarction (MI) is an emergency condition that requires immediate medical treatment. The rapid and accurate diagnosis of MI using a 12-lead electrocardiogram (ECG) is extremely important in a clinical study to save the patient’s life. The manual interpretation of MI using a 12-lead ECG is tedious and time-consuming. Therefore, a patient-specific software-based computer-aided diagnosis framework is helpful to detect and localize MI disease accurately. This paper proposes a patient-specific higher-order tensor-based approach to detect and localize MI automatically using 12-lead ECG recordings. The 12-lead ECG recordings are segmented into 12-lead ECG beats using the multi-lead fusion-based QRS detection algorithm. The fast and adaptive multivariate empirical mode decomposition (FA-MVEMD) based multiscale analysis method decomposes 12-lead ECG beat into a third-order tensor containing the information from the samples, beat, and intrinsic mode functions (IMFs). Furthermore, a fourth-order tensor is formulated by considering beats, samples, lead, and IMFs information of 12-lead ECG recording. The multilinear singular value decomposition (MLSVD) extracts features from the fourth-order tensors and third-order tensors of 12-lead ECG. The K-nearest neighbor (KNN), support vector machine (SVM), and stacked autoencoder-based deep neural network (SAE-DNN) models are used for the detection and localization of MI using fourth-order and third-order tensor domain features. The proposed approach is evaluated using 73 healthy control (HC) and 100 different types of MI-based 12-lead ECG recordings from a public database. The proposed approach has obtained the classification accuracy values of (98.84%, 98.27%, 98.27%) and (86.64%, 83.17%, and 81.98%) using (KNN, SVM, and SAE-DNN) models for MI detection, and localization, respectively using 30-min duration of 12-lead ECG recordings. For MI detection and localization, the suggested approach has obtained accuracy values of 96.53% and 93.32%, respectively, using the 4-s duration of 12-lead ECG recordings. Our approach outperformed existing MI detection and localization methods using 12-lead ECG recordings regarding classification performance.
Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%).
Cardiovascular diseases (CVDs) are one of the principal causes of death. Cardiac arrhythmia, a critical CVD, can be easily detected from an electrocardiogram (ECG) recording. Automated ECG analysis can help clinicians to identify arrhythmia and prevent untimely death. This paper presents a simple model to classify the ECG recordings into two classes: Normal and Abnormal based on morphological and heart rate variability (HRV) features. Before feature extraction, Signal quality analysis (SQA) is performed to abandon poor quality ECG signals. Several machine-learning classifiers such as Support Vector Machine (SVM), Adaboost (AB), Random Forest (RF), Extra-Tree Classifier (ET), Decision Tree (DT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Gradient Boosting (GB) are explored on the extracted feature space. To enhance the study, few feature selection algorithms such as F test, Least Absolute Shrinkage and Selection Operator (LASSO), and Minimal Redundancy Maximal Relevance (mRMR) algorithms are also applied and the outcomes of each algorithm along with the considered classifiers are analyzed and compared. The proposed algorithm is validated on 2648 Normal and 2518 Abnormal ECG recordings. The accuracy of our best classifier is found to be 95.25 %. It is anticipated that the proposed model will be helpful as a primary and mass screening tool kit in clinical settings.
The automatic detection and classification of life-threatening arrhythmia is life-threatening in treatment of a variety of cardiac diseases. Cardiac arrhythmias are irregular heartbeats that are either too fast (tachycardia) or too slow (bradycardia). Minor alteration in the morphology or dynamics of the Electrocardiogram (ECG) can induce severe arrhythmia events, that can decrease the heart's ability to pump blood and cause breathing difficulties, chest pain, tiredness, and loss of consciousness. A unique deep learning technique for classification of distinct types of arrhythmia utilizing feature extraction is provided in this research. To acquire morphological features, the Shannon Entropy Morlet Wavelet Transform (SEMWT) is done to every heart beat. This work uses Empirical Mode Decompositions (EMDs) with Fuzzy Weight Beetle Swarm Optimization (FWBSO) is introduced for signal noise removal. Then, utilising Kullback-Leibler Divergence Kernel Principal Component Analysis (KLDKPCA) and Dynamic Time Wrapping ECG segments are selected, whereas morphological features from P-QRS-T waves are extracted using SEMWT. SEMWT improves the time and frequency resolution of an ECG signal, making it easier to decode critical information. ECG signal was divided into low-frequency approximation and high-frequency detail components after applying SEMWT. Then, with high accuracy, a Kernel Weight Convolutional Neural Network (KWCNN)-based automated arrhythmia classification is constructed. This work’s resulted are evaluated with performance metrics of Sensitivity (SEN), F-measure, Positive Predictivity (PP) and Accuracy (ACC). Over the whole MIT-BIH Arrhythmias Database, the suggested approach was tested. The suggested classification approach was first tested in MATLAB, with the results compared to those of other methods.
Health monitoring in humans is very important. This monitoring can be done in different people from embryonic period to adulthood. A healthy fetal will lead to a healthy baby. For this purpose, health assessment methods are used from the fetal to adulthood. One of the most common methods of assessing health at different times is to use clinical signs and data. Measuring heart rate, blood pressure, temperature, and other symptoms can help monitor health. However, there are usually errors in human predictions. Data mining is a technique for identifying and diagnosing diseases, categorizing patients in disease management, and finding patterns to diagnose patients more quickly and prevent complications. It could be a great help. Increasing the accuracy of diagnosis, reducing costs, and reducing human resources in the medical sector have been proven by researchers as the benefits of introducing data mining in medical analysis. In this paper, data mining methods will be introduced to diagnose and monitor health in individuals with various heart diseases. Heart disease will be evaluated to make the study more comprehensive, including fetal health diagnosis, arrhythmias, and machine learning data mining angiography. Attempts are made to introduce the relevant database in each disease and to evaluate the desired methods in health monitoring. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Extraction of significant features from Electrocardiogram (ECG) signal is the primary concern for accurate diagnosis of cardiac arrhythmia. This work presents a novel approach of multilevel feature analysis and deep learning strategy for efficient ECG beat classification. The multilayer characteristics of ECG signal obtained from Empirical mode decomposition (EMD) are explored to extract discriminative feature vectors. The multilayer similarity coefficients are obtained by applying Dynamic time warping (DTW) metric and Pearson correlation coefficient (PCC) as diagnostic features. Furthermore, discrete orthonormal Stockwell transform (DOST) is employed for time-frequency representation of ECG data in multilayer aspect. The sublet changes in time-frequency spaces due to the presence of cardiac abnormalities are captured by estimating various nonlinear parameters. Interlayer deviations of these nonlinear parameters are estimated as the significant characteristics of arrhythmia detection. In addition, this study shows that the phase synchrony (PS) coefficients are prominent index for quantifying the crucial phase variation between normal and abnormal heart conditions. Hence multilevel PS coefficients are employed as the predictors of arrhythmia detection. Finally, the extracted feature vectors are fed to various classifiers to identify the heart anomalies. The proposed technique attains average accuracy of 98.82% and 98.14% using support vector machine (SVM) and k-nearest neighbors (k-NN) classifier respectively. The improved classification accuracy of 99.05% is obtained with the strategy of combining deep neural network (DNN) with the proposed feature extraction policy. Present work delivers satisfactory and superior performances for arrhythmia classification compare to other existing approaches.
As researches on computer-aided arrhythmia detection deepen, the application in clinical practice is still challenging due to weak generalization ability. The utilization of the existing prior patient knowledge can be an effective approach to address the issue and to ultimately architect a network appropriate for clinical use. An inverted residual block-embedded deep neural network (IRBEDNN) is proposed to accurately detect arrhythmias based on processed ECGs. Firstly, continuous ECGs are segmented into individual heartbeats based on R peak information. To simulate the real clinical scenario, these heartbeats are converted into 2-dimensional heartbeat images without any signal processing. Then, selected heartbeats are fed into the proposed IRBEDNN which combines CNN and inverted residual block (IRB) to extract implicit features. Also, patient-specific knowledge is exploited in the network to enhance arrhythmia detection. Finally, 24 data records from the MIT-BIH arrhythmia database are utilized to validate the method’s effectiveness and superiority. The effect on utilizing different duration of patient-specific information to the final experimental results is investigated and analyzed in detail, which demonstrates the effectiveness of using patient-specific information. On the 24 data-segment tests, the highest classification accuracy could reach 100%, while the overall ACC is 96.326%, higher than that of the existing comparison models. The precision of the S class can reach 0.816, also higher than comparison methods. After the boosting method, the recall reaches 0.852 for the S class and 0.921 for the V class, which outperforms the other comparison methods on average. Ablation experiments is conducted to investigate the performance of the IRBEDNN. Results show that the proposed IRBEDNN-based method achieves good generalization ability with promising accuracy on both the MIT-BIH arrhythmia database and the INCARTDB. Therefore, the proposed IRBEDNN could accurately and efficiently detect arrhythmias and has positive significance to clinical applications.
Medical imaging diagnosis is the most assisted method to help physicians diagnose patient diseases using different imaging test modalities. In fact, Deep learning aims to simulate human cognitive functions. It providing a paradigm shift in the field of medical imaging, due to the expanding availability of medical imaging data and to the advancing deep learning techniques. In effect, deep learning algorithms have become the approach of choice for medical imaging, from image acquisition to image retrieval, from segmentation to disease prediction. In our paper, we present a review that focuses on exploring the application of deep learning in medical imaging from different perspectives.
An expert-knowledge attention network (EKANet) was designed to improve the accuracy of arrhythmia diagnosis and reduce the recheck time. This network classifies four tachyarrhythmia on electrocardiogram (ECG) signals, encompassing most arrhythmia diseases. In the EKANet, two attention modules based on the knowledge of cardiology can rapidly capture the ECG rhythm and P waves in multiple leads without any training. This mechanism is performed to reduce the computational time of re-building a model. The EKANet integrates a six-layer convolutional neural network (CNN) and a gated recurrent unit (GRU) as the classifier to realise the tachyarrhythmia classification. The EKANet outperformed 1D CNN and ArrhythmiaNet on the MIT-BIH datasets by 3.1% on average accuracy. Furthermore, the EKANet achieved approximately 8.5% and 3.9% average F1-score increases on the dataset of China ECG challenge contest compared with time-incremental CNN (TI-CNN) and attention-based TI-CNN, respectively. Meanwhile, the EKANet has a much lower complexity than that of the other typical models with a competitive accuracy.
Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias. This work proposes a computer-aided diagnosis (CAD) system to automatically classify different types of arrhythmias from ECG signals. First, the auto-encoder convolutional network (ACN) model is used, which is based on a one-dimensional convolutional neural network (1D-CNN) that automatically learns the best features from the raw ECG signals. After that, the support vector machine (SVM) classifier is applied to the features learned by the ACN model to improve the detection of arrhythmic beats. This classifier detects four different types of arrhythmias, namely the left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat (PB), and premature ventricular contractions (PVC), along with the normal sinus rhythms (NSR). Among these arrhythmias, PVC is particularly a dangerous type of heartbeat in ECG signals. The performance of the model is measured in terms of accuracy, sensitivity, and precision using a tenfold cross-validation strategy on the MIT-BIH arrhythmia database. The obtained overall accuracy of the SVM classifier was 98.84%. The result of this model is portrayed as a better performance than in other literary works. Thus, this approach may also help in further clinical studies of cardiac cases.
Elderly persons are generally prone to CHDs (Chronic Heart Diseases). Arrhythmia is a persistent CHD with high mortalities resulting from cardiac failures, heart strokes, and CADs (Coronary Artery Diseases). Arrhythmia can be detected using ECG (Electrocardiogram) signals. ECG signals need to be pre-processed for removing noises present in signals. Since denoising is a significant step in ECG signals. Recently Support Vector Machine -Radial Bias Function (SVM-RBF) classifier is introduced for arrhythmia classification, it doesn’t remove noises presented from the ECG signals. The major aim of the work is to design a new classifier with removed noises and enhanced ECG signal. In this work, EMDs (Empirical Mode Decompositions) is introduced for noise removing which works recursively and dependent on signals called sifting. In EMD, IMFs (Intrinsic Mode Functions) decompose noisy signals into intrinsic oscillatory components adaptively using sifting. Further, FWBSOs (Fuzzy Weight Beetle Swarm Optimizations) are used in this work for optimizing EMDs and IMFs. This work in the initial phase reconstructs ECG signals which are filtered by IMFs. These filters are followed by extraction of morphological features from waves of P-QRS-T while ECG segments are selected using PCAs and DTWs. In the final phase, EKSVMs (Enhanced Kernel Support Vector Machines) classifies extracted features automatically by categorizing ECG signals into Normal and Ventricular Ectopic Beats. This work’s resulted are evaluated with performance metrics of Sensitivity, F-measure, Positive Productivity and Accuracy. This work uses database of MIT-BIH arrhythmia in a 5 fold cross validation for its predictions. The proposed EKSVMs classifier is compared to existing classifiers such as K-Nearest Neighbors (KNN), Enhanced Particle Swarm Optimisation-Multiple Layer Perception (EPSO-MLP) and SVM-RBF. The experiments of the proposed classifier and existing methods are carried out on MATLAB R2018a.
This paper introduces a heartbeat classification system that combines three types of neural networks: random neural networks, deep autoencoders and RBF neural networks. The aim is to make use of the advantages of these neural networks in order to introduce a model with simpler architecture than the state-of-the-art deep models. Indeed, the advantages of the three combined networks, briefly, are these: (i) Autoencoders provide high level features without pre-processing; (ii) Random neural networks provide good generalisation and very fast training; (iii) RBF neural networks provide high coverage of the input space and allow using prior knowledge. On the other hand, two types of features are used: coded features (obtained from the autoencoder) and RR interval based-features. To evaluate the performance of the proposed system, we conduct experiments on the MIT-BIH arrhythmia dataset and we consider the recommendations of the association for the advancement of medical instrumentation, which defines five classes of interest. Furthermore, the experiments are based on an inter-patient paradigm and the obtained results are compared with some of the state-of-the-art methods.
Cardiovascular diseases such as cardiac arrhythmia are a leading cause of death worldwide. Detecting cardiac arrhythmias before their occurrence using an Electrocardiogram (ECG) signal helps in risk stratification, better medical assistance, and patient treatment. Due to privacy concerns, access to personal ECGs is restricted, hindering the development of automated computer-aided diagnosis systems. This chapter discusses an approach for generating irregular beats, namely, supraventricular ectopic, ventricular ectopic, and normal beats recommended by the Association for the Advancement of Medical Instrumentation using a Conditional Generative Adversarial Network (CGAN). Irregular beat generation is necessary due to their rare occurrence that stems from complex biological, physiological systems. CGAN demonstrates its feasibility and effectiveness by discovering intricate structures present in heartbeats by augmenting specific class beats and improving the diagnostic performance of arrhythmia classification. A Convolution Neural Network based generator and discriminator is employed that incorporates the class information and conventional input for generating beats. Four publicly available standard datasets are adopted for verification of the proposed approach. The developed arrhythmia classifier also achieved better diagnostic performance than state-of-the-art models. The quality of generated signals is estimated quantitatively through five evaluation metrics and qualitatively through visual representation.
Background and objectives: Most deep-learning-related methodologies for electrocardiogram (ECG) classification are focused on finding an optimal deep-learning architecture to improve classification performance. However, in this study, we proposed a methodology for fusion of various single-lead ECG data as training data in the single-lead ECG classification problem.
Methods: We used a squeeze-and-excitation residual network (SE-ResNet) with 152 layers as the baseline model. We compared the performance of a 152-layer SE-ResNet trained on ECG signals from various leads of a standard 12-lead ECG system to that of a 152-layer SE-ResNet trained on only single-lead ECG data with the same lead information as the test set. The experiments were performed using five different types of rhythm-type single-lead ECG data obtained from Konkuk University Hospital in South Korea.
Results: Experiment results based on the combination from the relationship experiments of the leads showed that lead –aVR or II revealed the best classification performance. In case of -aVR, this model achieved a high F1 score for normal (98.7%), AF (98.2%), APC (95.1%), and VPC (97.4%), indicating its potential for practical use in the medical field.
Conclusion: We concluded that the 152-layer SE-ResNet trained by fusion of single-lead ECGs had better classification performance than the 152-layer SE-ResNet trained on only single-lead ECG data, regardless of the single-lead ECG signal type. We also found that the best performance directions for single-lead ECG classification are Lead -aVR and II.
In this paper, an automated heartbeat classification has been proposed to prevent the growing threats of cardiovascular diseases around the world. The MIT BIH Arrhythmia database has been used for the training and testing of the proposed approach. The database contains 90095 normal (N), 2781 supraventricular (SVEB), 7008 ventricular (VEB), 802 fusions (F) and 15 unclassified (Q) beats each of 30 minutes duration. Total 61 features have been extracted using the time series feature extraction library (TSFEL). Feature selection or reduction methods applied are feature scaling, removal of highly correlated and low variance features, and Random Forest Recursive Feature Elimination (RF-RFE). The methodological novelty of this study is mainly incorporation of TSFEL during feature extractions, Synthetic Minority Over-sampling Technique (SMOTE) to create a balanced dataset, an ensemble of RF and Support Vector Machine (SVM) using Weighted Majority Algorithm (WMA) for heartbeat classification to improve the results. Grid Search has been performed to optimize the hyper-parameters of RF and SVM classifiers. A final evaluation has been carried out considering a 'subject-specific scheme. The sensitivity that our approach has achieved for the arrhythmic heartbeat classes are as follows: N: 99.50%, SVEB (S): 74.20%, VEB (V): 94.22%, F: 73.21%, and Q: 0%. The corresponding positive predictive values are: N: 98.67%, SVEB (S): 90.09%, VEB (V): 95.95 %, F: 88.35%, Q: 0%. In comparison with machine learning (ML) and deep learning (DL) based state-of-the-art approaches, significant improvement in efficiency have been found.
Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels abnormality that serves as a global leading reason for death. The earlier the abnormal heart rhythm is discovered, the less severe the sequela and the faster the recovery. Electrocardiogram (ECG), as a main way to detect the electrical activity of heart, is a very important harmless means of predicting and diagnosing CVDs. However, ECG signal has characteristics of complex and high chaos, making it time-consuming and exhausting to interpret ECG signal even for experts. Hence, computer-aided methods are required to relief human burden and reduce errors caused by tiredness, inter- and intra-difference. Deep learning shows outstanding performance on ECG classification studies recent few years. Its hierarchical architecture enables higher-level features obtained and its strong ability to feature extraction contributes to classification project. Latest studies can achieve higher accuracy and efficiency than manual classification by experts. In this paper, we review the existing studies of deep learning applied in ECG diagnosis according to four typical algorithms: stacked auto-encoders, deep belief network, convolutional neural network and recurrent neural network. We first introduced the mechanism, development and application of the algorithms. Then we review their applications in ECG diagnosis systematically, discussing their highlights and limitations. Our view about future potential development of deep learning in ECG diagnosis is stated in the final part of this paper.
Diabetes mellitus, a chronic disease associated with elevated accumulation of glucose in the blood, is generally diagnosed through an invasive blood test such as oral glucose tolerance test (OGTT). An effective method is proposed to test type 2 diabetes using peripheral pulse waves, which can be measured fast, simply and inexpensively by a force sensor on the wrist over the radial artery. A self-designed pulse waves collection platform includes a wristband, force sensor, cuff, air tubes, and processing module. A dataset was acquired clinically for more than one year by practitioners. A group of 127 healthy candidates and 85 patients with type 2 diabetes, all between the ages of 45 and 70, underwent assessments in both OGTT and pulse data collection at wrist arteries. After preprocessing, pulse series were encoded as images using the Gramian angular field (GAF), Markov transition field (MTF), and recurrence plots (RPs). A four-layer multi-task fusion convolutional neural network (CNN) was developed for feature recognition, the network was well-trained within 30 minutes based on our server. Compared to single-task CNN, multi-task fusion CNN was proved better in classification accuracy for nine of twelve settings with empirically selected parameters. The results show that the best accuracy reached 90.6% using an RP with threshold
$\epsilon$
of 6000, which is competitive to that using state-of-the-art algorithms in diabetes classification.
It is necessary to explore the feature segments of electrical representation of heart signals i.e. ECG signals for the arrhythmia identification. A feature cluster extraction based classification is proposed in our research for detection and identification of rhythm class. After the feature cluster extraction of signal record it is compared with the set of rules framework for rhythm class assignment. The work has been validated and compare with relevant state of art methods and found improved performance with respect to sensitivity Se and positive predictivity Pp parameters for ventricular and supraventricular rhythm class. Proposed approach achieved sensitivity (Se) as 94.25% and positive predictivity (Pp) as 96.35% and sensitivity (Se) 86.5% and positive predictivity (Pp) 83.47% for ventricular rhythm and supraventricular rhythm respectively for dataset DS2. As a result we achieved significant improvement after comparison with other methods for the MIT-BIHA database.
Heartbeat classification is central to the detection of the arrhythmia. For the effective heartbeat classification, the noise-robust features are very significant. In this work, we have proposed a noise-robust support vector machine (SVM) based heartbeat classifier. The proposed classifier utilizes a novel noise-robust morphological feature which is based on the conditional spectral moment (CSM) of the heartbeat. In addition to the proposed CSM feature, we have also employed the existing RR interval, the wavelets, and the higher-order statistics (HOS) based temporal and morphological feature sets. The noise-robustness test of the proposed CSM and all the studied feature sets is performed for the SVM based heartbeat classifier. Further, we have studied the significance of combining these temporal and morphological features on the final classification performance. For this purpose, the individual SVMs were trained for each of the feature set. The final classification is based on the ensemble of these individual SVMs. Various combining scheme such as sum, majority, and product rules are employed to ensemble the result of the individually trained SVMs. The experimental results show the noise-robustness of the proposed CSM feature. The proposed classifier gives improved overall performance compared to the existing heartbeat classification systems.
The heartbeat class detection of the electrocardiogram is important in cardiac disease diagnosis. For detecting morphological QRS complex, conventional detection algorithm have been designed to detect P, QRS, T wave. However, the detection of the P and T wave is difficult because their amplitudes are relatively low, and occasionally they are included in noise. We applied two morphological feature extraction methods: higher-order statistics and Hermite basis functions. Moreover, we assumed that the QRS complexes of class N and S may have a morphological similarity, and those of class V and F may also have their own similarity. Therefore, we employed a hierarchical classification method using support vector machines, considering those similarities in the architecture. The results showed that our hierarchical classification method gives better performance than the conventional multiclass classification method. In addition, the Hermite basis functions gave more accurate results compared to the higher order statistics.
The objective of this work is to develop a model for ECG classification based on multilead features. The MIT-BIH Arrhythmia database was used following AAMI recommendations and class labeling. We used for classification classical features as well as features extracted from different scales of the wavelet decomposition of both leads integrated in an RMS manner. Step-wise and a randomized method were considered for feature subset selection, and linear discriminant analysis (LDA) was also used for additional dimensional reduction. Three classifiers: linear, quadratic and Mahalanobis distance were evaluated, using a k-fold like cross validation scheme. Results in the training set showed that the best performance was obtained with a 28-feature subset, using LDA and a Mahalanobis distance classifier. This model was evaluated in the test dataset with the following performance measurements global accuracy: 86%; for supraventricular beats, Sensitivity: 86%, Positive pred.: 20%; for ventricular beats Sensitivity: 71%, Positive pred.: 61%. This results show the feasibility of classification based on the multilead wavelet features, although further development is needed in subset selection and classification algorithms.
Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbal- anced datasets in which negative instances heavily outnumber the positive in- stances (e.g. in gene profiling and detecting credit card fraud). This paper dis- cusses the factors behind this failure and explains why the common strategy of undersampling the training data may not be the best choice for SVM. We then propose an algorithm for overcoming these problems which is based on a vari- ant of the SMOTE algorithm by Chawla et al, combined with Veropoulos et al's different error costs algorithm. We compare the performance of our algorithm against these two algorithms, along with undersampling and regular SVM and show that our algorithm outperforms all of them.
L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classifica- tion problems, particularly ones with many features. L1 regularized logistic regression requires solving a convex optimization problem. However, standard algorithms for solving convex optimization problems do not scale well enough to handle the large datasets encountered in many practical settings. In this paper, we propose an efficient algorithm for L1 regularized logistic regres- sion. Our algorithm iteratively approximates the objec- tive function by a quadratic approximation at the current point, while maintaining the L1 constraint. In each iter- ation, it uses the efficient LARS (Least Angle Regres- sion) algorithm to solve the resulting L1 constrained quadratic optimization problem. Our theoretical results show that our algorithm is guaranteed to converge to the global optimum. Our experiments show that our algo- rithm significantly outperforms standard algorithms for solving convex optimization problems. Moreover, our algorithm outperforms four previously published algo- rithms that were specifically designed to solve the L1 regularized logistic regression problem.
L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm. In this paper we compare state-of-the-art optimization tech- niques to solve this problem across several loss functions. Furthermore, we propose two new techniques. The first is based on a smooth (differen- tiable) convex approximation for the L1 regularizer that does not depend on any assumptions about the loss function used. The other technique is a new strategy that addresses the non-differentiability of the L1-regularizer by casting the problem as a constrained optimization problem that is then solved using a specialized gradient projection method. Extensive comparisons show that our newly proposed approaches consistently rank among the best in terms of convergence speed and efficiency by measur- ing the number of function evaluations required.
Supervised and interpatient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function. Several classification models able to cope with the strong class unbalance and a large variety of feature sets have been proposed for this task. In practice, over 200 features are often considered, and the features retained in the final model are either chosen using domain knowledge or an exhaustive search in the feature sets without evaluating the relevance of each individual feature included in the classifier. As a consequence, the results obtained by these models can be suboptimal and difficult to interpret. In this work, feature selection techniques are considered to extract optimal feature subsets for state-of-the-art ECG classification models. The performances are evaluated on real ambulatory recordings and compared to previously reported feature choices using the same models. Results indicate that a small number of individual features actually serve the classification and that better performances can be achieved by removing useless features.
In this paper, we studied and validated a simple heartbeat classifier based on ECG feature models selected with the focus on an improved generalization capability. We considered features from the RR series, as well as features computed from the ECG samples and different scales of the wavelet transform, at both available leads. The classification performance and generalization were studied using publicly available databases: the MIT-BIH Arrhythmia, the MIT-BIH Supraventricular Arrhythmia, and the St. Petersburg Institute of Cardiological Technics (INCART) databases. The Association for the Advancement of Medical Instrumentation recommendations for class labeling and results presentation were followed. A floating feature selection algorithm was used to obtain the best performing and generalizing models in the training and validation sets for different search configurations. The best model found comprehends eight features, was trained in a partition of the MIT-BIH Arrhythmia, and was evaluated in a completely disjoint partition of the same database. The results obtained were: global accuracy of 93%; for normal beats, sensitivity (S) 95%, positive predictive value (P(+)) 98%; for supraventricular beats, S 77%, P(+) 39%; and for ventricular beats S 81%, P(+) 87%. In order to test the generalization capability, performance was also evaluated in the INCART, with results comparable to those obtained in the test set. This classifier model has fewer features and performs better than other state-of-the-art methods with results suggesting better generalization capability.
An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NN's are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.
This paper presents a new solution to the expert system for reliable heartbeat recognition. The recognition system uses the support vector machine (SVM) working in the classification mode. Two different preprocessing methods for generation of features are applied. One method involves the higher order statistics (HOS) while the second the Hermite characterization of QRS complex of the registered electrocardiogram (ECG) waveform. Combining the SVM network with these preprocessing methods yields two neural classifiers, which have been combined into one final expert system. The combination of classifiers utilizes the least mean square method to optimize the weights of the weighted voting integrating scheme. The results of the performed numerical experiments for the recognition of 13 heart rhythm types on the basis of ECG waveforms confirmed the reliability and advantage of the proposed approach.
In this paper, we developed and evaluated a robust single-lead electrocardiogram (ECG) delineation system based on the wavelet transform (WT). In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia, QT, European ST-T and CSE databases, developed for validation purposes. The QRS detector obtained a sensitivity of Se = 99.66% and a positive predictivity of P+ = 99.56% over the first lead of the validation databases (more than 980,000 beats), while for the well-known MIT-BIH Arrhythmia Database, Se and P+ over 99.8% were attained. As for the delineation of the ECG waves, the mean and standard deviation of the differences between the automatic and manual annotations were computed. The mean error obtained with the WT approach was found not to exceed one sampling interval, while the standard deviations were around the accepted tolerances between expert physicians, outperforming the results of other well known algorithms, especially in determining the end of T wave.
A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50,000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.
The prompt and adequate detection of abnormal cardiac conditions by computer-assisted long-term monitoring systems depends greatly on the reliability of the implemented ECG automatic analysis technique, which has to discriminate between different types of heartbeats. In this paper, we present a comparative study of the heartbeat classification abilities of two techniques for extraction of characteristic heartbeat features from the ECG: (i) QRS pattern recognition method for computation of a large collection of morphological QRS descriptors; (ii) Matching Pursuits algorithm for calculation of expansion coefficients, which represent the time-frequency correlation of the heartbeats with extracted learning basic waveforms. The Kth nearest neighbour classification rule has been applied for assessment of the performances of the two ECG feature sets with the MIT-BIH arrhythmia database for QRS classification in five heartbeat types (normal beats, left and right bundle branch blocks, premature ventricular contractions and paced beats), as well as with five learning datasets-one general learning set (GLS, containing 424 heartbeats) and four local sets (GLS+about 0.5, 3, 6, 12 min from the beginning of the ECG recording). The achieved accuracies by the two methods are sufficiently high and do not show significant differences. Although the GLS was selected to comprise almost all types of appearing heartbeat waveforms in each file, the guaranteed accuracy (sensitivity between 90.7% and 99%, specificity between 95.5% and 99.9%) was reasonably improved when including patient-specific local learning set (sensitivity between 94.8% and 99.9%, specificity between 98.6% and 99.9%), with optimal size found to be about 3 min. The repeating waveforms, like normal beats, blocks, paced beats are better classified by the Matching Pursuits time-frequency descriptors, while the wide variety of bizarre premature ventricular contractions are better recognized by the morphological descriptors.
The aim of this paper is twofold. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats. Second, we propose a novel classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the basis of ECG data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In particular, they were organized so as to test the sensitivity of the SVM classifier and that of two reference classifiers used for comparison, i.e., the k-nearest neighbor (kNN) classifier and the radial basis function (RBF) neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. On an average, over three experiments making use of a different total number of training beats (250, 500, and 750, respectively), the PSO-SVM yielded an overall accuracy of 89.72% on 40438 test beats selected from 20 patient records against 85.98%, 83.70%, and 82.34% for the SVM, the kNN, and the RBF classifiers, respectively.
The diagnosis of cardiac dysfunctions requires the analysis of long-term ECG signal recordings, often containing hundreds to thousands of heart beats. In this work, automatic inter-patient classification of heart beats follow-ing AAMI guidelines is investigated. The prior of the normal class is by far larger than the other classes, and the classifier obtained by a standard SVM training is likely to act as the trivial acceptor. To avoid this inconvenience, a SVM classi-fier optimizing a convex approximation of the balanced classification rate rather than the standard accuracy is used. First, the assessment of feature sets previ-ously proposed in the litterature is investigated. Second, the performances of this SVM model is compared to those of previously reported inter-patient classifi-cation models. The results show that the choice of the features is of major im-portance, and that some previously reported feature sets do not serve the clas-sification performances. Also, the weighted SVM model with the best feature set selection achieves results better than previously reported inter-patient models with features extracted only from the R spike annotations.
We consider supervised learning in the presence of very many irrelevant features, and study two different regularization methods for preventing overfitting. Focusing on logistic regression, we show that using in the number of irrelevant features.
Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
Recent work on Conditional Random Fields (CRFs) has demonstrated the need for regularisation when applying these models to real-world NLP data sets. Conventional approaches to regularising CRFs has focused on using a Gaussian prior over the model parameters. In this paper we explore other possibilities for CRF regularisation. We examine alternative choices of prior distribution and we relax the usual simplifying assumptions made with the use of a prior, such as constant hyperparameter values across features. In addition, we contrast the effectiveness of priors with an alternative, parameter-free approach. Specifically, we employ logarithmic opinion pools (LOPs). Our results show that a LOP of CRFs can outperform a standard unregularised CRF and attain a performance level close to that of a regularised CRF, without the need for intensive hyperparameter search.
The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.
A photocurrent technique was used to accurately measure dose enhancement in the gate oxide of MOS devices with tungsten or titanium silicide over various thicknesses of poly-Si exposed to low-energy x-irradiation. The results show that the dose enhancement is strongly dependent on the type of metal/silicide used and the thickness of the poly-Si layer between the metal/silicide and the SiO2 gate insulator. A straightforward procedure for calculating the equal damage dose equivalence for metal/silicide over poly-Si gate MOS structures is also presented.
Presents the application of the fuzzy neural network for
electrocardiographic (ECG) beat recognition and classification. The new
classification algorithm of the ECG beats, applying the fuzzy hybrid
neural network and the features drawn from the higher order statistics
has been proposed in the paper. The cumulants of the second, third, and
fourth orders have been used for the feature selection. The hybrid fuzzy
neural network applied in the solution consists of the fuzzy
self-organizing subnetwork connected in cascade with the multilayer
perceptron, working as the final classifier. The c-means and
Gustafson-Kessel algorithms for the self-organization of the neural
network have been applied. The results of experiments of recognition of
different types of beats on the basis of the ECG waveforms have
confirmed good efficiency of the proposed solution. The investigations
show that the method may find practical application in the recognition
and classification of different type heart beats
This tutorial provides an overview of the basic theory of hidden
Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and
gives practical details on methods of implementation of the theory along
with a description of selected applications of the theory to distinct
problems in speech recognition. Results from a number of original
sources are combined to provide a single source of acquiring the
background required to pursue further this area of research. The author
first reviews the theory of discrete Markov chains and shows how the
concept of hidden states, where the observation is a probabilistic
function of the state, can be used effectively. The theory is
illustrated with two simple examples, namely coin-tossing, and the
classic balls-in-urns system. Three fundamental problems of HMMs are
noted and several practical techniques for solving these problems are
given. The various types of HMMs that have been studied, including
ergodic as well as left-right models, are described
We consider supervised learning in the presence of very many irrelevant features, and study two di#erent regularization methods for preventing overfitting. Focusing on logistic regression, we show that using L 1 regularization of the parameters, the sample complexity (i.e., the number of training examples required to learn "well,") grows only logarithmically in the number of irrelevant features.
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.