Bilateral Signal Warping for Left Ventricular Hypertrophy Diagnosis

Left Ventricular Hypertrophy (LVH) is a major cardiovascular risk factor, linked to heart failure, arrhythmia, and sudden cardiac death, often resulting from chronic stress like hypertension. Electrocar-diography (ECG), while varying in sensitivity, is widely accessible and cost-effective for detecting LVH-related morphological changes. This work introduces a bilateral signal warping (BSW) approach to improve ECG-based LVH diagnosis. Our method creates a library of heartbeat prototypes from patients with consistent ECG patterns. After preprocessing to eliminate baseline wander and detect R peaks, we apply BSW to cluster heartbeats, generating prototypes for both normal and LVH classes. We compare each new record to these references to support diagnosis. Experimental results show promising potential for practical application in clinical settings.

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Publication Details

Published
2025-04-14
DOI
https://doi.org/10.1109/isbi60581.2025.10980912
Citations
1
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
1.21

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article

Bilateral Signal Warping for Left Ventricular Hypertrophy Diagnosis

Raymond H. Chan, Wei Tang, Jean‐Michel Morel, Kangning Cui
1 citations
Fault Detection and Control Systems
1.21
article

Bilateral Signal Warping for Left Ventricular Hypertrophy Diagnosis

Raymond H. Chan, Wei Tang, Jean‐Michel Morel, Kangning Cui
article en
1 citations

Abstract

Left Ventricular Hypertrophy (LVH) is a major cardiovascular risk factor, linked to heart failure, arrhythmia, and sudden cardiac death, often resulting from chronic stress like hypertension. Electrocar-diography (ECG), while varying in sensitivity, is widely accessible and cost-effective for detecting LVH-related morphological changes. This work introduces a bilateral signal warping (BSW) approach to improve ECG-based LVH diagnosis. Our method creates a library of heartbeat prototypes from patients with consistent ECG patterns. After preprocessing to eliminate baseline wander and detect R peaks, we apply BSW to cluster heartbeats, generating prototypes for both normal and LVH classes. We compare each new record to these references to support diagnosis. Experimental results show promising potential for practical application in clinical settings.

City University of Hong Kong (HK)
Lamar University, Innovation and Technology Fund
Openalex Percentile: Top 22%
Fault Detection and Control Systems
1.21
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