Topological Data Analysis Approach to Extract the Persistent Homology Features of Ballistocardiogram Signal in Unobstructive Atrial Fibrillation Detection

• Published in: IEEE sensors journal Vol. 22; no. 7; pp. 6920 - 6930
• Main Authors: Jiang, Fangfang, Xu, Bowen, Zhu, Ziyu, Zhang, Biyong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrhythmia; atrial fibrillation; Ballistocardiogram signal; Ballistocardiograms; Bar codes; Cardiac arrhythmia; Classification; Data analysis; Diagnosis; Electrocardiography; Feature extraction; Fibrillation; Homology; Machine learning; Nonlinear dynamics; persistent homology features; phase space reconstruction; Rhythm; Sensors; Time series analysis; Time-frequency analysis; topological data analysis; Topology; Trajectory; unobstructive detection

Atrial fibrillation (AF) is the most common arrhythmia, which requires long-term diagnosis and treatment in a daily routine. Recently, ballistocardiogram (BCG), an unobstructive cardiac function monitoring method, is widely studied for AF detection. Usually, the AF classification performance depends...