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Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal

Authors :
Tianyi Ling
Ziyu Zhu
Yanbing Zhang
Fangfang Jiang
Source :
Applied Sciences, Vol 12, Iss 20, p 10370 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF.

Details

Language :
English
ISSN :
20763417
Volume :
12
Issue :
20
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.ffa815543238401abb93dd880a632321
Document Type :
article
Full Text :
https://doi.org/10.3390/app122010370