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Long-term ECG recordings are often required for the monitoring of the cardiac function in clinical applications. Due to the high number of beats to evaluate, inter-patient computer-aided heart beat classification is of great importance for physicians. The main difficulty is the extraction of discriminative features from the heart beat time series....
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... Use of the RR interval ratio can reduce the overlap between S and class N heartbeat and thus increase the S detection rate. First the ECG heart-beat time series are divided into two data sets one for training and another for testing as used by Lannoy [10] as shown in Table II. The two sets of heart beat time series are transformed into R-peak aligned time time series as proposed in Section IIIA and is shown in Figure 2 for the test record 213. ...
... The classification experiments were conducted based on the division of MIT-BIH records into two data sets as used by Chazal [6] and Lannoy [10] is shown in Table II. For the comparative results shown in Table III, we considered the papers which used the complete data of 44 records of MIT-BIH Arrhythmia Database excluding the other 4 records containing the paced beats of the respective 4 patients as recommended by AAMI standard. ...
An ElectroCardiogram (ECG) inter-patient heartbeat time series classification method by a hierarchical system of based on support vector machine and Decision rule, using full heart-beat time series by alignment of R-peaks of all beats, is proposed. PQRST Time series of heart-beats having converted into equal length series by alignment of R-peaks of all heart-beats based on R-peak of largest length PQRST series in the data and by padding zeroes to the smaller length series on either side, was used in this experimentation. The main objective of this paper is to identify the abnormalities in ECG heart beats based on AAMI Categorization. Experiments were conducted on ECG data of 44 patients obtained from MIT-BIH Arrhythmia database. Results were compared with existing methods such as weighted support vector machine (SVM), hierarchical SVM and weighted linear discriminant analysis (LDA). Comparative analysis confirms the viability and superiority of the proposed approach in terms of Total classification accuracy (TCA). Proposed system achieved Sensitivities of 98.7%, 85.9%, 88.8%, 58.3%, PPV% of 98.53%, 82.2%, 89.9%, 85.6% for N, S, V and F classes respectively and a TCA of 97.3%.
... Apart from the RR interval, which is used in most studies, almost every single published paper proposes a new set of features to be used, or a new combination of the existing ones. Among others, morphological features extracted directly from the ECG like amplitudes and peak widths [9], [32], features based on different transforms, e.g., wavelet transform (WT) [15], [33], [34], principal component analysis [26], [35] and Hermite functions [23], [29], [31], as well as statistical features, e.g., variances [14], [36], have been proposed. Due to the large number of available features, some authors have already availed themselves of feature selection (FS) methods to reduce the dimensionality of the classification problem [6], [7], [15], [23], [29], [37], [38]. ...
... Among others, morphological features extracted directly from the ECG like amplitudes and peak widths [9], [32], features based on different transforms, e.g., wavelet transform (WT) [15], [33], [34], principal component analysis [26], [35] and Hermite functions [23], [29], [31], as well as statistical features, e.g., variances [14], [36], have been proposed. Due to the large number of available features, some authors have already availed themselves of feature selection (FS) methods to reduce the dimensionality of the classification problem [6], [7], [15], [23], [29], [37], [38]. These methods involve a process wherein a number of subsets of the available features are evaluated, and the best one is selected for application on the learning algorithm. ...
... However, in order to foster common proceedings, the Association for the Advancement of Medical Instrumentation (AAMI) proposed a standard for the evaluation of ECG classifiers [44], which recommends to group all present morphologies in six classes according to their physiological origin (see Table I). From these six classes, the standard recommends to ignore records containing paced beats when evaluating classifiers, and some authors have proposed to ignore unknown beats too, for being too poorly represented and of no help for further classification purposes [6], [7], [28], [29]. Applied to the MITDB, this standard leaves 44 records which should be divided using an interpatient division scheme such as the one proposed in [5] if a realistic estimation of the realworld performance is desired. ...
This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57:1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring.
... Since the SVM can manage high-dimensional and large datasets this classifier constitutes a suited choice for many tasks which are related to biomedical classification problems [11]. Facial expression classification [27], text classification [58], beat detection [46] and QRS complex classification [23] are only a few examples for successful applications of the SVM. ...
... To reduce the contribution of dominating classes in the training process one has to weight the parameter C by adding a new parameter w i for each class [23]. ...
... The linear Kernel has been chosen because it is computationally easiest to apply. Furthermore, applying this Kernel one just needs to find the optimal parameter C. Moreover this, the linear Kernel turned out to be even more powerful compared to more complex Kernels in other investigations [23]. ...
In this paper, one-dimensional Discrete Anamorphic Stretch Transform is proposed as an additional pre-processor for the feature extraction of the ECG signal using discrete wavelet transform in order to enhance the arrhythmia classification accuracy. Three DAST kernels: linear, sublinear, and superlinear kernels are proposed for enhancing the morphological features of the QRS complex. Its effectiveness is evaluated using two classifiers: feed-forward-based neural network and support vector machine with radial basis function. The MIT–BIH arrhythmia database and the generic cardiac beat classes such as normal (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F) and unknown beat (Q) are used for evaluating the proposed pre-processor. The training and testing of the classifier follow an inter-patient as well as intra-patient procedures. The classifier with SVM_RBF and the proposed pre-processor using DAST result in an increase in the average accuracy, sensitivity, specificity, positive predictivity, F-score and overall accuracy by 1.29%, 15.63%, 3.7%, 35.7%, 20.66%, and 2.796%, respectively, compared to that without DAST. The percentage improvement in the above performance metrics using ANN Classifier with DAST is 2.99%, 27.73%, 6.83%, 64.27%, 31.53% and 6.48%, respectively, compared to that without DAST. The morphological features obtained using DAST and DWT are also combined with RR-interval features. The combined feature set is found to have better classification accuracy than that using only morphological features. The accuracy of the proposed classifier is also found to be improved compared to many of the standard ECG classifiers reported in the literature.
Blind Source Separation approaches have proved their efficiency to solve problems dealing with recovering a set of underlying sources from recoded observations without any a priori knowledge on the mixture process and sources. For this reason, we propose to use them to extract the true fetal ECG signal and consequently to calculate its instantaneous heart rate. Thus, we aim the application of the Robust Second-Order Blind Identification (RSOBI) algorithm, which exploits non-stationarity properties and second-order statistics, to a set of ECG mixtures recorded on pregnant mother. The obtained results show that we can separate original mixtures into 3 main sources which can be considered as the fetal ECG, the maternal ECG and noise. The recovered fetal ECG signals were found very clean and have permitted to perform fetal instantaneous heart rate calculation with a high precision.KeywordsBlind source separationFetal ECGInstantaneous heart rateRSOBI
Electrocardiography (ECG) is a test at that checks the electric activity of the coronary heart. Arrhythmia is a sort of coronary heart ailment characterized through abnormal heartbeats. The prognosis is primarily based totally at the hobby of the R top withinside the ECG sign. The maximum not unusual place sort of arrhythmia is atrial fibrillation. The heartbeat will become abnormal and fast because of this. In order to stay a healthful life, it is far important to repair a everyday coronary heart rhythm. The present-day technique of detecting arrhythmia is to connect the tool to a lead and ship an ECG sign to a health practitioner, while the occasion is occurring. The uncooked ECG sign acquired from the present-day database is preprocessed the use of the FFT filter. The diploma of the polynomial equation is decided through the wide variety of factors that have to match in an effort to create an easy curve that replicates the HRV sign A polynomial diploma of n = 6 equation yields the high-quality outcomes in becoming the HRV sign. Statistical and wavelet parameters are mixed right into an unmarried set of parameters in hybrid with curve becoming. The performance of the proposed set of rules is in comparison with that of different algorithms and evaluated on an MIT database. The proposed offline technique has an accuracy of 94%.KeywordsSignalFeaturesClassifiersFFT
An arrhythmia classification model based on an adaptive boosting algorithm is proposed in this paper. According to the AAMI standard, 15 kinds of abnormal cardiac rhythms are grouped and the datasets are segmented by the non-crossover method. The electrocardiogram (ECG) signals are denoised by the filter method, and then divided into fixed-length ECG beats, and five features are extracted from time-domain and frequency-domain. Then, the base classifier of the algorithm and its optimal algorithm parameters is selected to realize the multi-classification of cardiac anomalies, aiming at mining hidden knowledge from human physiological data to detect human health status, making the diagnosis process more automatic, efficient, and intelligent.
The report of World Health Organization (WHO) specifies that the diagnosis and treatment of cardiovascular diseases are challenging tasks. To study the electrical conductivity of the heart, Electrocardiogram (ECG) which is an inexpensive diagnostic tool, is used. Classification is the most well-known topic for arrhythmia detection related to cardiovascular disease. Many algorithms have been evolved for the classification of heartbeat arrhythmia in the previous few decades using the CAD system. In this paper, we have developed a new deep CNN (11-layer) model for automatically classifying ECG heartbeats into five different groups according to the ANSI-AAMI standard (1998) without using feature extraction and selection techniques. The experiment is performed on publicly available Physionet MIT-BIH database and evaluated results are then compared with the existing works mentioned in the literature. To handle the problem of minority classes as well as the class imbalance problem, the database has been oversampled artificially using SMOTE technique. The augmented ECG database was employed for training the model while the testing was performed on the unseen dataset. On evaluation of the results from the experiment, we found that the proposed CNN model performed better in comparison to the experiments mentioned in other papers in terms of accuracy, sensitivity, and specificity. abstract environment.
Objective:
To train convolutional networks using multi-lead ECG data and classify new data accurately to provide reliable information for clinical diagnosis.
Methods:
The data were pre-processed with a bandpass filter, and signal framing was adopted to adjust the data of different lengths to the same size to facilitate network training and prediction. The dataset was expanded by increasing the sample size to improve the detection rate of abnormal samples. A depth-wise separable convolution structure was used for more specific feature extraction for different channels of twelve-lead ECG data. We trained the two classifiers for each label using the improved DenseNet to classify different labels.
Results:
The propose model showed an accuracy of 80.13% for distinguishing between normal and abnormal ECG with a sensitivity of 80.38%, a specificity of 79.91% and a F1 score of 79.35%.
Conclusions:
The model proposed herein can rapidly and effectively classify the ECG data. The running time of a single dataset on GPU is 33.59 ms, which allows real-time prediction to meet the clinical requirements.
