Preprint Article Version 1 This version is not peer-reviewed

Persistence Landscape based Topological Data Analysis for Personalized Arrhythmia Classification

Version 1 : Received: 29 August 2019 / Approved: 30 August 2019 / Online: 30 August 2019 (09:51:40 CEST)

How to cite: Yan, Y.; Ivanov, K.; Cen, J.; Liu, Q.; Wang, L. Persistence Landscape based Topological Data Analysis for Personalized Arrhythmia Classification. Preprints 2019, 2019080320 (doi: 10.20944/preprints201908.0320.v1). Yan, Y.; Ivanov, K.; Cen, J.; Liu, Q.; Wang, L. Persistence Landscape based Topological Data Analysis for Personalized Arrhythmia Classification. Preprints 2019, 2019080320 (doi: 10.20944/preprints201908.0320.v1).

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.

Subject Areas

Electrocardiography Analysis; Persistence Landscape; Signal Analysis; Machine Learning;Topological Data Analysis; Topological Signal Signature; Classification; Time Series Analysis; Biomedical Signal Analysis; Persistence Homology

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