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

• Autorzy: Devi Reeta , Tyagi Hitender Kumar , Kumar Dinesh

Identyfikatory

DOI

10.1016/j.bbe.2019.05.011

• Języki publikacji: EN

Abstrakty

EN

Sudden 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 min before occurrence was obtained as 83.33%. Therefore, this study suggests a new efficient approach for the early-stage prediction of SCD that is considerably different from that reported in the literature to date. However, to generalize the findings, the algorithm needs to be tested for a larger population group.

Słowa kluczowe

EN

CWT; ECOC; heart rate variability; multi-class classification; sudden cardiac death; weighted KNN

PL

CWT; ECOC; zmienność rytmu serca; nagły zgon sercowy

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 3
• Strony: 586--598
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Devi Reeta

• Kurukshetra University, Haryana, India

autor

Tyagi Hitender Kumar

• Kurukshetra University, Haryana, India

autor

Kumar Dinesh

• J.C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).