A novel multi-class approach for early-stage prediction of sudden cardiac death

Publication date: Available online 1 June 2019Source: Biocybernetics and Biomedical EngineeringAuthor(s): Reeta Devi, Hitender Kumar Tyagi, Dinesh KumarAbstractSudden cardiac death (SCD) is a complex issue that may occur in population groups with either known or unknown cardiovascular disease (CVD). Given the complex nature of SCD, the discovery of a suitable biomarker will prove essential in identifying individuals at risk of SCD, while discriminating it from patients with other cardiac pathologies as well as healthy individuals. Thus, this study aimed to develop an efficient approach to support a better comprehension of heart rate variability (HRV) as a predictive biomarker to identify SCD patients at an early stage. The present study proposed a novel multi-class classification approach using signal processing methods of HRV to predict SCD 10 min before its occurrence. The developed algorithm was qualitatively and quantitatively analyzed in terms of discriminating SCD patients from patients of heart failure and normal people. A total of 51 HRV signals of all three classes obtained from PhysioBank were processed to extract 32 features in each subject. The optimal feature selection was performed by a hybrid approach of sequential feature selection-random under sampling boosting algorithms. Multi-class classifiers, namely decision tree, support vector machine, and k-nearest neighbors were used for classification. An average classification accuracy of SCD prediction 10&nbs...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research