A novel approach to predict sudden cardiac death (SCD) using nonlinear and time-frequency analyses from HRV signals.

Abstract

Investigations show that millions of people all around the world die as the result of sudden cardiac death (SCD). These deaths can be reduced by using medical equipment, such as defibrillators, after detection. We need to propose suitable ways to assist doctors to predict sudden cardiac death with a high level of accuracy. To do this, Linear, Time-Frequency (TF) and Nonlinear features have been extracted from HRV of ECG signal. Finally, healthy people and people at risk of SCD are classified by k-Nearest Neighbor (k-NN) and Multilayer Perceptron Neural Network (MLP). To evaluate, we have compared the classification rates for both separate and combined Nonlinear and TF features. The results show that HRV signals have special features in the vicinity of the occurrence of SCD that have the ability to distinguish between patients prone to SCD and normal people. We found that the combination of Time-Frequency and Nonlinear features have a better ability to achieve higher accuracy. The experimental results show that the combination of features can predict SCD by the accuracy of 99.73%, 96.52%, 90.37% and 83.96% for the first, second, third and forth one-minute intervals, respectively, before SCD occurrence.