No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
ECG Arrhythmia Classification By Using Convolutional Neural Network And Spectrogram
... They also represent the same information that Cardiologists use to interpret ECG recordings. Other than the standard derived and morphological features, the spectrogram of the ECG signal was proposed by [13,14] for rhythm and heartbeat classification. Compared to the standard features, a spectrogram contains information from both the time and frequency domains. ...
... As one can see in Table 4, the proposed ResNet models are better than the standard ResNet models in all metrics. Moreover, the proposed ResNet Models also performed better than Sen's algorirhm [14] which is only using spectrogram and CNN. Sen's algorithm achieved 99.57% sensitivity, 2.99% false-alarm rate, 96.72% positive predictive value and 98.21% accuracy detecting three types of heartbeats. ...
Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients' heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.
... Limin Yu et al. used the CNN approach using 48 MIT-BIH database recordings to classify the ECG signal to diagnose cardiac illness, with an accuracy of 97.63 percent [11]. Sena Y.S. et al. used Limin Yu in the same way and obtained 99.67 percent accuracy [12]. ...
Abstract:Heart disease is one of the leading causes of death in the world. Congestive Heart Failure (CHF) is one type of heart disease that needs attention. CHF is a condition in which the heart cannot pump blood adequately throughout the body. This disease usually affects patients over the age of 60 years. An EKG can be used to diagnose this condition. However, doctors need to diagnose manually, namely, reading the ECG signal directly. Therefore, this study aims to create a system that can diagnose CHF automatically using the 1D convolutional neural network (CNN) method. This CNN 1D method uses normalization as preprocessing, three hidden layers with 16 output channels, a fully connected layer, and sigmoid activation. The research dataset comes from MIT-BIH and BIDMC. Based on this study, 100% accuracy results were obtained with recall, precision, and 1 F1-Score, respectively, so this study can assist medical staff in identifying CHF conditions and providing appropriate therapy to patients
... Hannun et al. [7] collected 91,232 single-lead ECGs from 53,549 individuals who used a single-lead ambulatory ECG monitoring device to create a deep neural network (DNN) to distinguish 12 rhythm categories. Ş EN et al. [8] proposed ECG time-series signal classification and ECG spectrogram pictures to classify heartbeat arrhythmia. Ullah et al. [9] proposed a two-dimensional (2-D) convolutional neural network (CNN) model in order to classify ECG signals into eight classes and achieved an accuracy of 99.11 percent. ...
Manual interpretation and classification of ECG signals lack both accuracy and reliability. These continuous time-series signals are more effective when represented as an image for CNN-based classification. A continuous Wavelet transform filter is used here to get corresponding images. In achieving the best result generic CNN architectures lack sufficient accuracy and also have a higher run-time. To address this issue, we propose an ensemble method of transfer learning-based models to classify ECG signals. In our work, two modified VGG-16 models and one InceptionResNetV2 model with added feature extracting layers and ImageNet weights are working as the backbone. After ensemble, we report an increase of 6.36% accuracy than previous MLP-based algorithms. After 5-fold cross-validation with the Physionet dataset, our model reaches an accuracy of 99.98%.
... Por otra parte, (Schwab et al., 2017;Limam and Precioso, 2017;Singh et al., 2018;Mostayed et al., 2018;Banerjee et al., 2019;Park and Yun, 2019;Simanjuntak et al., 2020) hicieron uso de la RNR, la MCP fue utilizada por (Gao et al., 2019;Hou et al., 2019;Yildirim et al., 2019;Saadatnejad et al., 2019;Kim and Pyun, 2020;Wang, 2021). Finalmente, como ya se había mencionado con anterioridad las CNN tienen una mayor aportación con los trabajos realizados por (Kachuee et al., 2018b;Yıldırım et al., 2018;Jun et al., 2018;Salem et al., 2018;Ş en andÖzkurt, 2019;Izci et al., 2019;Liu et al., 2019;Rohmantri and Surantha, 2020;Wang et al., 2020;Atal and Singh, 2020;Ferretti et al., 2021;Mathunjwa et al., 2021;Zhang et al., 2021). Surgen también la combinación entre dos ADL, es decir, la creación de modelos híbridos. ...
Una arritmia cardíaca es un latido irregular del corazón que se traduce en un impulso eléctrico anormal, y su tipo se define por el ritmo y duración. Su clasificación ha sido abordada en diferentes campos de la ciencia, destacando el uso de algoritmos de aprendizaje profundo. La presente investigación, utilizó un modelo híbrido entre Redes Neuronales Convolucionales y el algoritmo metaheurístico de Optimización por Enjambre de Partículas; para la clasificación de arritmias cardíacas. El metaheurístico se encargó de optimizar la arquitectura de capas de la red neuronal, a través de la minización de la pérdida durante el entrenamiento y prueba. Los datos se obtuvieron del MIT-BIH Arrhythmia dataset, donde se describen cinco categorías de arritmias. Los resultados logrados demostraron que el metaheurístico es un algoritmo confiable en la búsqueda de la mejor arquitectura de capas, logrando obtener una exactitud del 97%, lo que significa que el uso de técnicas metaheurísticas es una opción que se debe tomar en consideración a la hora de optimizar el rendimiento de las redes neuronales convolucionales.
... Interest in the implementation of artificial intelligence methods in the field of broadly understood classification is constantly growing [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], which is caused by the continuous improvement of learning algorithms and the increase in the computing power of computers [29][30][31][32][33][34]. The conventional approach to digital signals processing with the use of convolutional artificial neural networks (CNNs) is the signal transition from the time domain to the time-frequency domain, i.e., to the signal image [35][36][37][38], which takes into account a typical structure of the convolutional neural network (CNN) [25,26,39], which is much easier to process than the raw signal. ...
With the increasing complexity of the electromagnetic environment and continuous development of radar technology we can expect a large number of modern radars using agile waveforms to appear on the battlefield in the near future. Effectively identifying these radar signals in electronic warfare systems only by relying on traditional recognition models poses a serious challenge. In response to the above problem, this paper proposes a recognition method of emitted radar signals with agile waveforms based on the convolutional neural network (CNN). These signals are measured in the electronic recognition receivers and processed into digital data, after which they undergo recognition. The implementation of this system is presented in a simulation environment with the help of a signal generator that has the ability to make changes in signal signatures earlier recognized and written in the emitter database. This article contains a description of the software’s components, learning subsystem and signal generator. The problem of teaching neural networks with the use of the graphics processing units and the way of choosing the learning coefficients are also outlined. The correctness of the CNN operation was tested using a simulation environment that verified the operation’s effectiveness in a noisy environment and in conditions where many radar signals that interfere with each other are present. The effectiveness results of the applied solutions and the possibilities of developing the method of learning and processing algorithms are presented by means of tables and appropriate figures. The experimental results demonstrate that the proposed method can effectively solve the problem of recognizing raw radar signals with agile time waveforms, and achieve correct probability of recognition at the level of 92–99%.
... Among them, comparison algorithms (Hwang et al (2018); Şen et al (2019);and Tan et al (2018)) is added. ...
In order to solve the problem that the traditional convolution neural network-based arrhythmia classification model does not extract the time series feature deeply, and ECG is a typical time series signal. This paper introduces the long short-term memory (LSTM) network to improve it. A classification algorithm of arrhythmia based on CNN-LSTM network model is proposed. Firstly, the deep CNN is designed to encode the ECG signals and extract the morphological features of ECG signals. Secondly, through the temporal correlation of LSTM learning morphological feature representation, the intrinsic features are deeply mined. The automatic classification of arrhythmia based on the characteristics of ECG signal is realized. Finally, the experimental results based on the MIT-BIH arrhythmia database show that this method significantly shortens the classification time. The classification accuracy rate is over 96%. Moreover, the mean positive retrieval rate and sensitivity are 91% and 92% respectively. The proposed method has strong noise resistance and achieves ideal data analysis performance of health IoT monitoring.
... Other examples include using deep learning for the automatic recognition of ECG signals [102], a convolutional neural network (CNN) for arrhythmia classification in [103] and AF in [104], and recurrent neural network (RNN) for activity prediction [105]. Different machine learning algorithms have been applied in IoT Cloud-based monitoring systems in [13] and Cloud-based cardiology services in [106]. ...
Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.
... Other examples include using deep learning for the automatic recognition of ECG signals [102], a convolutional neural network (CNN) for arrhythmia classification in [103] and AF in [104], and recurrent neural network (RNN) for activity prediction [105]. Different machine learning algorithms have been applied in IoT Cloud-based monitoring systems in [13] and Cloud-based cardiology services in [106]. ...
Background and objectives:
Mobile and ubiquitous devices are everywhere, generating an exorbitant amount of data. New generations of healthcare systems are using mobile devices to continuously collect large amounts of different types of data from patients with chronic diseases. The challenge with such Mobile Big Data in general, is how to meet the growing performance demands of the mobile resources handling these tasks, while simultaneously minimizing their consumption.
Methods:
This research proposes a scalable architecture for processing Mobile Big Data. The architecture is developed around three new algorithms for the effective use of resources in performing mobile data processing and analytics: mobile resources optimization, mobile analytics customization, and mobile offloading. The mobile resources optimization algorithm monitors resources and automatically switches off unused network connections and application services whenever resources are limited. The mobile analytics customization algorithm attempts to save energy by customizing the analytics processes through the implementation of some data-aware schemes. Finally, the mobile offloading algorithm uses some heuristics to intelligently decide whether to process data locally, or delegate it to a cloud back-end server.
Results:
The three algorithms mentioned above are tested using Android-based mobile devices on real Electroencephalography (EEG) data streams retrieved from sensors and an online data bank. Results show that the three combined algorithms proved their effectiveness in optimizing the resources of mobile devices in handling, processing, and analyzing EEG data.
Conclusion:
We developed an energy-efficient model for Mobile Big Data which addressed key limitations in mobile device processing and analytics and reduced execution time and limited battery resources. This was supported with the development of three new algorithms for the effective use of resources, energy saving, parallel processing and analytics customization.
An electrocardiogram measures the electrical activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non‐invasive nature. It is possible to detect some of the heart's abnormalities by analyzing the electrical signal of each heartbeat, which is the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, as it is challenging to visually detect heart disease from the ECG signals. Implementing an automated ECG signal detection system can aid in the identification of arrhythmia and increase diagnostic accuracy. In this chapter, we proposed ECG signal (continuous electrical measurement of the heart), implemented, and compared multiple types of deep learning models to predict heart arrhythmias for classifying normal signals and abnormal signals. The MIT‐BIH arrhythmia dataset has been used. Finally, authors have discussed the limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges.
It is still a challenge to develop an electrocardiography (ECG) interpreter based on ECG basic characteristics because of the uncertainty of ECG delineation. Based on the clinical investigation in this study, ECG devices generated interpretations of Atrial Fibrillation (AF), Premature Ventricular Contraction (PVC), and Premature Atrial Contraction (PAC) have high ratios of false-positive errors. An ECG interpretation gap exists between ECG devices and cardiologists. This study aimed to develop an ECG interpreter to improve the performance of AF, PVC, and PAC based on clinical ECGs. This study first adopted a deep learning model to delineate ECG features such as P, QRS, and T waves based on 1160 8–10-s lead I or lead II ECG signals from a clinically-used 12-lead ECG device whose ECG device interpretation is AF as a training dataset. Second, a sliding window with 3-RR intervals in length is applied to the raw ECG to examine the delineated features in the window, and the ECG interpretation is then determined based on the experiences of cardiologists. The results indicate the following: (1) This delineator achieves good performance on P-, QRS-, and T- wave delineation with a sensitivity/specificity of 0.94/0.98, 1.00/0.99, and 0.97/0.98, respectively, in 48 10-s test ECGs mixed with true-positive AF and false-positive AF ECGs. (2) As compared to ECG-device generated interpretations, the precision of the detection of AF, PVC, and PAC in this study was increased from 0.77 to 0.86, 0.76 to 0.84, and 0.82 to 0.87 in 188 10-s test ECGs. Finally, (3) the F1 measure, which is a measure of the accuracy of test data but takes false- positives and false-negative into account, on the detection of AF, PVC, and PAC were 0.92, 0.91, and 0.83, respectively. In conclusion, this study overcomes the difficulties of ECG P-wave discrimination between true-positive AF and false-positive AF which are not documented well in previous research, and improves the precision of ECG devices’ interpretation. We believe that this study can facilitate clinical applications of ECG, and bridge the gap between machines’ ECG interpretation and cardiologists.
Manual interpretation and classification of ECG signals lack both accuracy and reliability. These continuous time-series signals are more effective when represented as an image
for CNN-based classification. A continuous Wavelet transform filter is used here to get corresponding images. In achieving the best result generic CNN architectures lack sufficient accuracy and also have a higher run-time. To address this issue, we propose an ensemble method of transfer learning-based models to classify ECG signals. In our work, two modified VGG-16 models and one lnceptionResNetV2 model with added feature extracting layers and ImageNet weights are working as the backbone. After ensemble, we report an increase of 6.36% accuracy than previous MLP based algorithms. After 5-fold cross-validation with the Physionet dataset, our model reaches an accuracy of 99.98%.
The electrocardiogram (ECG) is one of the simplest and oldest tools to assess the heart condition of cardiac patients. Heart diseases have emerged as one of the leading causes of death all over the world. According to the world health organization (WHO), millions of people are dying every year from heart-related diseases. A classification model that can early detect arrhythmia will be able to reduce this number by manyfold. Many researchers are working in this area and proposed many deep learning and machine Learning based models for arrhythmia classification. These models have high accuracy but require a machine with high computational power. Hence, these models are not sustainable options for the practical field. In this paper, we have proposed a 1D Convolutional Neural Network (CNN) model with high accuracy and low computational complexity. Our proposed methodology is appraised on the MIT-BIH arrhythmia dataset. We achieved overall 98.25% accuracy into five classes with an f1 score of 98.24%, precision 97.58%, and recall 96.79% which is better than previous results classifying arrhythmia. We can claim that our proposed method is better than most other existing models because of the higher accuracy with a simple architecture that can be run on an edge device with relatively low hardware configuration.
Due to advancement of new edge medical technologies, many methods have been applied to solve medical issues including machine learning approach. Cardiac Arrhythmia is one of the common diseases which can be solved using various machine learning approaches. There are many approaches which have already been introduced to classify arrhythmia and abnormality detection. This paper has a solution, introduces supervised and unsupervised models in which supervised models generate a good classification result. However, in this paper, we have also introduced a deep neural network classifier and used for prediction of arrhythmia if present based on some predefined value. In this paper, we have also connected it to the user interface to which the native users can check the level of arrhythmia.
In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians’ confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. The fusion model multipathway processing time-frequency domain featured the introduction of decision tree probability analysis of frequency domain features, the regularization of GRU model parameters and weight control to improve the decision tree model output weights. The MIT-BIH arrhythmia database was used for validation. Results showed that the low-frequency band features dominated the model prediction. The fusion model had an accuracy of 98.31%, sensitivity of 96.85%, specificity of 98.81%, and precision of 96.73%, indicating its high reliability and clinical significance.
This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system.
An adaptive implementation of 1D Convolutional Neural Networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus such a patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device.
The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).
Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner.
Due to its simple and parameter invariant nature, the proposed system is highly generic and thus applicable to any ECG dataset.
Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it offers more promises and has therefore attracted significant research attention in recent years. Unlike many existing methods which assume images of a set to lie on a certain geometric surface, this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure. Specifically, a Template Deep Reconstruction Model (TDRM) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The initialized TDRM is then separately trained for images of each class and class-specific DRMs are learnt. Based on the minimum reconstruction errors from the learnt class-specific models, three different voting strategies are devised for classification. Extensive experiments are performed to demonstrate the efficacy of the proposed framework for the tasks of face and object recognition from image sets. Experimental results show that the proposed method consistently outperforms the existing state of the art methods.
In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were 99.307%, 99.274%, 99.854%, 98.344%, 99.441% and 99.883%, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.
Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.
In BCI (brain - computer interface) systems, brain signals must be processed to identify distinct activities that convey different mental states. We propose a new technique for the classification of electroencephalographic (EEG) steady-state visual evoked potential (SSVEP) activity for non-invasive BCI. The proposed method is based on a convolutional neural network that includes a Fourier transform between hidden layers in order to switch from the time domain to the frequency domain analysis in the network. The first step allows the creation of different channels. The second step is dedicated to the transformation of the signal in the frequency domain. The last step is the classification. It uses a hybrid rejection strategy that uses a junk class for the mental transition states and thresholds for the confidence values. The presented results with offline processing are obtained with 6 electrodes on 2 subjects with a time segment of 1s. The system is reliable for both subjects over 95%, with rejection criterion.
In this paper, a multi-stage network including two multilayer perceptron (MLP) and one self organizing map (SOM) networks is presented. The input of the network is a combination of independent features and the compressed ECG data. The proposed network as a form of data fusion, performs better than using the raw data or individual features. We classified six common ECG waveforms using ten ECG records of the MIT/BIH arrhythmia database. An average recognition rate of 0.883 was achieved within a short training and testing time.
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.
A simple algorithm using topological mapping has been developed
for a real-time detection of the QRS complexes of ECG signals. As a
measure of QRS complex energy, the authors used topological mapping from
one dimensional sampled ECG signals to two dimensional vectors. To
describe a change of curvature, the authors derive modified spatial
velocity (MSV), from MSV the authors can locate QRS complexes more
easily. The proposed algorithm consists of very small C-language
procedures which reliably recognize the QRS complexes. For evaluation
the authors used the MIT/BIH arrhythmia database. The proposed algorithm
provides a good performance, a 99.58% detection rate of QRS complexes, a
99.57% sensitivity and 99.87% positive predictivity, respectively
Recurrent neural networks (RNNs) are a powerful model for sequential
data. End-to-end training methods such as Connectionist Temporal
Classification make it possible to train RNNs for sequence labelling
problems where the input-output alignment is unknown. The combination of
these methods with the Long Short-term Memory RNN architecture has
proved particularly fruitful, delivering state-of-the-art results in
cursive handwriting recognition. However RNN performance in speech
recognition has so far been disappointing, with better results returned
by deep feedforward networks. This paper investigates \emph{deep
recurrent neural networks}, which combine the multiple levels of
representation that have proved so effective in deep networks with the
flexible use of long range context that empowers RNNs. When trained
end-to-end with suitable regularisation, we find that deep Long
Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT
phoneme recognition benchmark, which to our knowledge is the best
recorded score.
Sleep apnoea is a very common sleep disorder which is able to cause symptoms such as daytime sleepiness, irritability and poor concentration. This paper presents a combinational feature extraction approach based on some nonlinear features extracted from Electro Cardio Graph (ECG) Reconstructed Phase Space (RPS) and usually used frequency domain features for detection of sleep apnoea. Here 6 nonlinear features extracted from ECG RPS are combined with 3 frequency based features to reconstruct final feature set. The nonlinear features consist of Detrended Fluctuation Analysis (DFA), Correlation Dimensions (CD), 3 Large Lyapunov Exponents (LLEs) and Spectral Entropy (SE). The final proposed feature set show about 94.8% accuracy over the Physionet sleep apnoea dataset using a kernel based SVM classifier. This research also proves that using non-linear analysis to detect sleep apnoea can potentially improve the classification accuracy of apnoea detection system.
Electrocardiogram (ECG) is the P-QRS-T wave, representing the cardiac function. The information concealed in the ECG signal is useful in detecting the disease afflicting the heart. It is very difficult to identify the subtle changes in the ECG in time and frequency domains. The Discrete Wavelet Transform (DWT) can provide good time and frequency resolutions and is able to decipher the hidden complexities in the ECG. In this study, five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed namely: non-ectopic beats, supra-ventricular ectopic beats, ventricular ectopic beats, fusion betas and unclassifiable and paced beats. Three dimensionality reduction algorithms; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) were independently applied on DWT sub bands for dimensionality reduction. These dimensionality reduced features were fed to the Support Vector Machine (SVM), neural network (NN) and probabilistic neural network (PNN) classifiers for automated diagnosis. ICA features in combination with PNN with spread value (σ) of 0.03 performed better than the PCA and LDA. It has yielded an average sensitivity, specificity, positive predictive value (PPV) and accuracy of 99.97%, 99.83%, 99.21% and 99.28% respectively using ten-fold cross validation scheme.
Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. The subtle changes in amplitude and duration of ECG cannot be deciphered precisely by the naked eye, hence imposing the need for a computer assisted diagnosis tool. In this paper we have automatically classified five types of ECG beats of MIT-BIH arrhythmia database. The five types of beats are Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). In this work, we have compared the performances of three approaches. The first approach uses principal components of segmented ECG beats, the second approach uses principal components of error signals of linear prediction model, whereas the third approach uses principal components of Discrete Wavelet Transform (DWT) coefficients as features. These features from three approaches were independently classified using feed forward neural network (NN) and Least Square-Support Vector Machine (LS-SVM). We have obtained the highest accuracy using the first approach using principal components of segmented ECG beats with average sensitivity of 99.90%, specificity of 99.10%, PPV of 99.61% and classification accuracy of 98.11%. The system developed is clinically ready to deploy for mass screening programs.
A neural network model, called a “neocognitron”, is proposed for a mechanism of visual pattern recognition. It is demonstrated by computer simulation that the neocognitron has characteristics similar to those of visual systems of vertebrates.
The neocognitron is a multilayered network consisting of a cascade connection of many layers of cells, and the efficiencies of the synaptic connections between cells are modifiable. Self-organization of the network progresses by means of “learning-without-a-teacher” process: Only repetitive presentation of a set of stimulus patterns is necessary for the self-organization of the network, and no information about the categories to which these patterns should be classified is needed. The neocognitron by itself acquires the ability to classify and correctly recognize these patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns. If a stimulus pattern is presented at a different position or if the shape of the pattern is distorted, the responses of the cells in the intermediate layers, especially the ones near the input layer, vary with the shift in position or the distortion in shape of the pattern. However, the deeper the layer is, the smaller become the variations in cellular responses. Thus, the cells of the deepest layer of the network are not affected by the shift in position or the distortion in shape of the stimulus pattern.
In this paper, we present a new system for the classification of electrocardiogram (ECG) beats by using a fast least square support vector machine (LSSVM). Five feature extraction methods are comparatively examined in the 15-dimensional feature space. The dimension of the each feature set is reduced by using dynamic programming based on divergence analysis. After the preprocessing of ECG data, six types of ECG beats obtained from the MIT-BIH database are classified with an accuracy of 95.2% by the proposed fast LSSVM algorithm together with discrete cosine transform. Experimental results show that not only the fast LSSVM is faster than the standard LSSVM algorithm, but also it gives better classification performance than the standard backpropagation multilayer perceptron network.
In this paper, we propose a novel independent components (ICs) arrangement strategy to cooperate with the independent component analysis (ICA) method used for ECG beat classification. The ICs calculated with a regular ICA algorithm are re-arranged according to the L2 norms of the rows of the de-mixing matrix. The validity of this ICs arrangement strategy is discussed mathematically and testified experimentally. Only when the ICs are arranged in an appropriate order, we are able to select the first couple of components for the calculation of the most significant subset of bases to discriminate different types of ECG beats. The effectiveness and efficiency of the proposed method and three other ICs arrangement strategies are studied. Two kinds of classifiers, including probabilistic neural network and support vector machines, are used to evaluate the proposed method. ECG samples attributing to eight different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study. The experiment results demonstrate that the proposed ICs arrangement strategy outperforms the other strategies in reducing the number of features required for the classifiers. Among the many experimental setups, the scheme with the SVM classifier in conjugate with the log(cosh(·)) contrast function and the proposed ICs arrangement strategy requires only 17 ICs to achieve more than 98.7% classification accuracy and is determined to be most efficient. When comparing to the other methods in the literature, the proposed scheme outperforms the other methods in terms of effectiveness and efficiency. The impressive result validates the strategy in the selection of significant ICs subset and demonstrates the proposed scheme an effective and efficient approach in computer-aided diagnosis of heart diseases based on ECG signals.
In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG.
Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, we have detected on ECG Arrhythmias using principal component analysis (PCA) and least square support vector machine (LS-SVM). The approach system has two stages. In the first stage, dimension of ECG Arrhythmias dataset that has 279 features is reduced to 15 features using principal component analysis. In the second stage, diagnosis of ECG Arrhythmias was conducted by using LS-SVM classifier. We took the ECG Arrhythmias dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) machine learning database. Classifier system consists of three stages: 50–50% of training-test dataset, 70–30% of training-test dataset and 80–20% of training-test dataset, subsequently, the obtained classification accuracies; 96.86%, 100% ve 100%. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for ECG Arrhythmias disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too.
The MIT-BIH Arrhythmia Database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and it has been used for that purpose as well as for basic research into cardiac dynamics at about 500 sites worldwide since 1980. It has lived a far longer life than any of its creators ever expected. Together with the American Heart Association Database, it played an interesting role in stimulating manufacturers of arrhythmia analyzers to compete on the basis of objectively measurable performance, and much of the current appreciation of the value of common databases, both for basic research and for medical device development and evaluation, can be attributed to this experience. In this article, we briefly review the history of the database, describe its contents, discuss what we have learned about database design and construction, and take a look at some of the later projects that have been stimulated by both the successes and the limitations of the MIT-BIH Arrhythmia Database.
Automatic electrocardiogram (ECG) beat classification is essential to timely diagnosis of dangerous heart conditions. Specifically, accurate detection of premature ventricular contractions (PVCs) is imperative to prepare for the possible onset of life-threatening arrhythmias. Although many groups have developed highly accurate algorithms for detecting PVC beats, results have generally been limited to relatively small data sets. Additionally, many of the highest classification accuracies (> 90%) have been achieved in experiments where training and testing sets overlapped significantly. Expanding the overall data set greatly reduces overall accuracy due to significant variation in ECG morphology among different patients. As a result, we believe that morphological information must be coupled with timing information, which is more constant among patients, in order to achieve high classification accuracy for larger data sets. With this approach, we combined wavelet-transformed ECG waves with timing information as our feature set for classification. We used select waveforms of 18 files of the MIT/BIH arrhythmia database, which provides an annotated collection of normal and arrhythmic beats, for training our neural-network classifier. We then tested the classifier on these 18 training files as well as 22 other files from the database. The accuracy was 95.16% over 93,281 beats from all 40 files, and 96.82% over the 22 files outside the training set in differentiating normal, PVC, and other beats.
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
Real-Time Discrimination of Ventricular
- T Fourier-Transform
T. Fourier-transform, "Real-Time Discrimination of Ventricular,"
vol. 46, no. 2, pp. 179-185, 1999.