Key result
A novel method combining ECG morphological and segment features with SVM classified normal, supraventricular ectopic, ventricular ectopic, and fusion beats with 97.80% accuracy.
A novel machine learning method combining ECG morphology and segment features achieved 97.8% accuracy in classifying arrhythmias from the MIT-BIH database.
May aid automated ECG analysis; leaves open prospective clinical validation before practice change.
In this work, arrhythmia appearing with the presence of abnormal heart electrical activity is efficiently recognized and classified. A novel method is proposed for accurate recognition and classification of cardiac arrhythmias. Firstly, P-QRS-T waves is segmented from ECG waveform; secondly, morphological features are extracted from P-QRS-T waves, and ECG segment features are extracted from the selected ECG segment by using PCA and dynamic time warping(DTW); finally, SVM is applied to the features and automatic diagnosis results is presented. ECG data set used is derived from the MIT-BIH in which ECG signals are divided into the four classes: normal beats(N), supraventricular ectopic beats (SVEBs), ventricular ectopic beats (VEBs) and fusion of ventricular and normal (F). Our proposed method can distinguish N, SVEBs, VEBs and F with an accuracy of 97.80 percent. The sensitivities for the classes N, SVEBs, VEBs and F are 99.27, 87.47, 94.71, and 73.88 percent and the positive predictivities are 98.48, 95.25, 95.22 and 86.09 percent respectively. The detection sensitivity of SVEBs and VEBs has a better performance by combining proposed features than by using the ECG morphology or ECG segment features separately. The proposed method is compared with four selected peer algorithms and delivers solid results.
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Zhu et al. (2018) studied Cardiac arrhythmias. Proposed method combining morphological and ECG segment features with SVM vs. ECG morphology or segment features separately, and four peer algorithms was evaluated on Accuracy of distinguishing normal beats, supraventricular ectopic beats, ventricular ectopic beats, and fusion beats. A novel method combining ECG morphological and segment features with SVM classified normal, supraventricular ectopic, ventricular ectopic, and fusion beats with 97.80% accuracy.
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