Prediction of Sudden Cardiac Death using support vector machine

Sudden death from cardiac arrest is a major health problem and is responsible for almost half of all heart disease deaths. In Sudden Cardiac Death (SCD), the cardiac arrest occurs for a very short time which is preceded and followed by normal ECG. Thus, it is difficult to detect such conditions, using only ECG. This work predicts sudden cardiac arrest before 30 minutes of its occurrence on the basis of time domain and frequency domain features of Heart rate variability (HRV) obtained from ECG and using SVM classifier to classify SCD patient from Normal patient The database of cardiac patients obtained from physionet is used to check the validity of the proposed work Performance of SVM is better giving the classification efficiency of 88%.