Research on intelligent classification of ECG signals based onthreedomain features extraction and GS-SVM

Abstract:

Singledomain based feature extraction has been extensively studied and is used to detect and classify Arrhythmia recently. In fact, multidomain feature extraction tends to perform better in classification. In this paper, threedomain features are extracted from timedomain, frequencydomain and waveletdomain using preprocessed ECG signals taken from 48 data sets in MIT/BIH arrhythmia database. These features fully characterize the nature of the ECG signals from various aspects. In the final stage, ECG signals are classified into four classes by the normalized features combined with gridsearch based SVM, the overall accuracy and F1 score of the proposed method is 98.01% and 0.9800 respectively, it performs well in detection and classification of ECG signals and has better generalization against the most results reported so far.