Persistence Landscape-based Topological Data Analysis for Personalized Arrhythmia Classification

Human ECG sensing signals can be regarded as the observed variables of the human heart’s nonlinear dynamic system, which can effectively reflect the state changes of the heart system. They can be used for heart health monitoring and related disease identification. Due to the robust chaos, nonlinearity, and complexity of ECG signals, it is challenging to express them by standard features. Therefore, this paper proposes a nonlinear topological data analysis method to model ECG signals and extract nonlinear features for ECG anomaly detection. Firstly, we use the time delay embedding approach to map the ECG time series to the topological space for phase space reconstruction to form the ECG point cloud. Then, based on the point cloud information in space, the persistent homology method was used to construct the topological imprint of ECG data. Finally, the persistence landscape in the topological impression was extracted as the topological feature of the ECG signal for ECG anomaly detection. With only 20% of the total training dataset, it achieves a 100% accuracy for normal heartbeats, 98.75% for ventricular beats, 95.88% for supra-ventricular moments, and 91.97% for fusion beats. Thus the method can be trained for a single individual, allowing for personalized analysis systems. With the present study, TDA could be a valuable tool for biomedical signal analysis, with potential application in customized data processing.