Version 1
: Received: 29 August 2019 / Approved: 30 August 2019 / Online: 30 August 2019 (09:51:40 CEST)
Version 2
: Received: 1 August 2023 / Approved: 1 August 2023 / Online: 2 August 2023 (10:33:09 CEST)
Y. Liu, L. Wang and Y. Yan, "Persistence Landscape-based Topological Data Analysis for Personalized Arrhythmia Classification," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1-6, doi: 10.1109/BSN58485.2023.10331360.
Y. Liu, L. Wang and Y. Yan, "Persistence Landscape-based Topological Data Analysis for Personalized Arrhythmia Classification," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1-6, doi: 10.1109/BSN58485.2023.10331360.
Y. Liu, L. Wang and Y. Yan, "Persistence Landscape-based Topological Data Analysis for Personalized Arrhythmia Classification," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1-6, doi: 10.1109/BSN58485.2023.10331360.
Y. Liu, L. Wang and Y. Yan, "Persistence Landscape-based Topological Data Analysis for Personalized Arrhythmia Classification," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1-6, doi: 10.1109/BSN58485.2023.10331360.
Abstract
Data can be illustrated in shapes, and the shapes could provide insight for data modeling and information extraction. Topological data analysis provides an alternative insight in biomedical data analysis and knowledge discovery with the algebra topology tools. In present work, we study the application of topological data analysis for personalized electrocardiographic signal classification toward arrhythmia analysis. Using phase space reconstruction technique, the signal samples are converted into point clouds for topological analysis facility. With topological techniques the persistence landscapes from the point clouds are extracted as features to perform the arrhythmia classification task. We find that the proposed method is robust to the training set size, with only a training set size of 20% percents, the normal heartbeat class are 100% recognized, ventricular beats for 97.13%, supra-ventricular beats for 94.27% and fusion beats for 94.27% within the corresponding experiments. The property of keeping high performance when using smaller training sample proves that the proposed method is especially applicable to personalized analysis. With the present study, we show that the topological data analysis technique could be a useful tool in biomedical signal analysis, and provide powerful ability in personalized analysis.
Keywords
Electrocardiography Analysis; Persistence Landscape; Signal Analysis; Machine Learning;Topological Data Analysis; Topological Signal Signature; Classification; Time Series Analysis; Biomedical Signal Analysis; Persistence Homology
Subject
Medicine and Pharmacology, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
