Sudden Cardiac Death prediction system using Hybrid classifier

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 Hybrid classifier to classify SCD patient from Normal patient. The hybrid classifier consists of three classifiers, Probability neural network (PNN), K-nearest Neighbour (KNN) and Support Vector Machine (SVM). The decision of the three classifiers is combined using simple voting system. The database of cardiac patients and normal patients from physionet is used to check the validity of the proposed work.Performance of hybrid classifier is better giving the classification efficiency of 90 %.