Linear and nonlinear analyses for detection of sudden cardiac death (SCD) using ECG and HRV signals

Studies show that millions of people throughout the world lose their lives as the result of sudden cardiac death (SCD) each year. These deaths can be reduced by using medical equipment such as defibrillators. However, there is still an urgent need for a suitable way to predict SCD so that the doctors can take proper decisions for patients at risk. In this paper, we investigated a way to predict sudden cardiac death. To do this, we first extract the HRV signal from ECG signal and elicit informative nonlinear and time-frequency features. Then, the dimension of feature space is reduced by applying feature selection and finally, healthy persons and those at risk of SCDs are classified using MLP and KNN neural networks. To evaluate the capabilities of analytical methods in classification, we have compared the classification rates by using both separate and combined nonlinear and TF features. The results show that there are features in the HRV signal of patients prone to SCD before the onset of SCD, which noticeably differ from those of normal people. Another remarkable result to emerge from our analysis is that the combination of time-frequency and nonlinear features have a better ability to detect this evident difference. The proposed method demonstrates that four minutes prior to the occurrence of SCD, the signals of a normal person and one of at risk can be differentiated in an effective and a reliable manner, which in turn, can make possible the provision of timely treatments.