SVM classification of CWT signal features for predicting sudden cardiac death

Abstract

Sudden Cardiac Death (SCD) is a major health problem that is responsible for most of all the heart disease deaths. The Ventricular Tachyarrhythmia’s (VT’s), especially the Ventricular Fibrillation (VF) are the primary cause of the SCD’s. This paper presents a classification method using Support Vector Machine (SVM) algorithm for predicting if there is an SCD occurrence in a signal. This is carried out by comparing certain characteristic features of the ECG signal of a normal healthy person with that of the unhealthy patient prone to SCD. In the time domain, the ECG signal has to be monitored continuously for long hours, which is not feasible and moreover the cardiac arrest in SCD cases occurs for a very short time which is preceded and followed by normal ECG. The noise and some electrical disturbances during measurement also affect the signal feature measurements. So, the signal is transformed into another domain using Wavelet Transformation method (Continuous Wavelet Transform (CWT) to be precise) to extract certain features of the signal and study their pattern while comparing the abnormal ECG signal with that of a normally running ECG signal. The main features that were extracted are the R-peaks, R to R intervals, QRS complexes, QRS complex durations, T-Wave durations and the QT intervals. CWT was used to extract the features information from the ECG signals providing the base. The tests were performed on the data signals taken from the Physionet database [].