Automated prediction of sudden cardiac death using statistically extracted features from electrocardiogram signals

Abstract

Sudden cardiac death (SCD) is becoming a severe problem despite significant advancements in the usage of the information and communication technology (ICT) in the health industry. Predicting an unexpected SCD of a person is of high importance. It might increase the survival rate. In this work, we have developed an automated method for predicting SCD utilizing statistical measures. We extracted the intrinsic attributes of the electrocardiogram (ECG) signals using Hilbert-Huang and wavelet transforms. Then utilizing machine learning (ML) classifier, we are using these traits to automatically classify regular and SCD existing risks. Support vector machine (SVM), decision tree (DT), naive Bayes (NB), discriminate k-nearest neighbors (KNN), analysis (Disc.), as well as an ensemble of classifiers also utilized (Ens.). The efficiency and practicality of the proposed methods are evaluated using a standard database and measured ECG data obtained from 18 ECG records of SCD cases and 18 ECG records of normal cases. For the automated scheme, the set of features can predict SCD very fast that is, half an hour before the occurrence of SCD with an average accuracy of 100.0% (KNN), 99.9% (SVM), 98.5% (NB), 99.4% (DT), 99.5% (Disc.), and 100.0% (Ens.)