A change-point method for multi-lead electrocardiogram monitoring using weighted multivariate functional principal component analysis

Hesam Hafezalseheh, Mohammad Fathian (), Rassoul Noorossana, Yaser Zerehsaz and Kamran Heidari
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Hesam Hafezalseheh: Iran University of Science and Technology
Mohammad Fathian: Iran University of Science and Technology
Rassoul Noorossana: University of Central Oklahoma
Yaser Zerehsaz: Research and Development Department
Kamran Heidari: Loghman Hakim Hospital, Shahid Beheshti University of Medical Science

Health Care Management Science, 2025, vol. 28, issue 3, No 2, 357-380

Abstract: Abstract Cardiovascular diseases (CVDs) are one of the primary reasons for death worldwide. These diseases often occur due to the occlusion of coronary arteries, thereby leading to insufficient blood and oxygen supply that damages cardiac muscle cells. Electrocardiogram (ECG) signals which reflect heart electrical activity are being used for diagnosing various cardiac diseases. Typically, a standard ECG consists of 12 channels referred to as leads which enable practitioners to monitor heartbeats through different channels where each heartbeat lasts approximately 600 ms. The majority of studies focus on the classification and early diagnosis of arrhythmias. Although the current studies on change-point methods have acquired massive accuracy in detecting potential shifts during a multi-channel process, they lack flexibility in manually assigning more weights to the channels, which are of more importance for experts. This could be addressed by implementing the weighted multivariate functional principal component analysis (WMFPCA). The objective of this study is to develop a novel change-point detection method to monitor long-term cardiovascular treatment. A third-order tensor structure was employed to represent the 12-lead ECG data in three dimensions (beats × samples × leads). Exploiting intra-beat, inter-beat, and inter-lead correlations along with channel significance in the third-order tensor, the WMFPCA is incorporated into Hotelling’s $${T}^{2}$$ T 2 statistic to construct monitoring schemes. Simulation results show that the proposed approach outperforms the existing methods in monitoring multi-channel processes. Finally, applying the suggested model on a real-world dataset containing Myocardial Infarction (MI) subjects verifies the model.

Keywords: Multi-lead ECG; Cardiovascular diseases; Tensor; WMFPCA; Change-point detection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10729-025-09712-y

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