A study of complex network features for electrocardiograms and its Applications in atrial fibrillation recognition

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control Pub Date : 2024-11-30 DOI:10.1016/j.bspc.2024.107295

Hui Yan , Zhengyu Chen , Fa Zhu , Wei Zheng

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引用次数: 0

Abstract

Atrial fibrillation (AF) is one of the most common arrhythmias in clinics. The traditional diagnosis of AF mainly depends on physicians’ visual observation of electrocardiograms (ECGs), which is an inefficient, time-consuming, and laborious task. Rapidly evolving complex network principles have opened up a new avenue for studying AF rhythm recognition. This paper thoroughly analyzes seventeen existing network features and proposes three novel network features: local efficiency distribution entropy (E_DE), clustering coefficient distribution entropy (C_DE), and degree distribution entropy (D_DE). Different from the existing local efficiency entropy and clustering coefficient entropy, the three distribution entropy features can reflect probability distributions of network features. This paper compares E_DE, C_DE, and D_DE with existing network features by using T-test, box plots, and machine learning models to validate their effectiveness in AF rhythm recognition. The experiments on PhysioNet/CinC Challenge 2017 show that E_DE, C_DE, and D_DE are superior to existing network features and the accuracy of AF recognition can achieve 94.96%, 94.72% and 95.58%, respectively, when using time-domain features plus E_DE, C_DE, and D_DE.

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来源期刊

Biomedical Signal Processing and Control 工程技术-工程：生物医学

CiteScore

9.80

自引率

13.70%

发文量

822

审稿时长

4 months

期刊介绍： Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.