Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment

• URL: http://arxiv.org/abs/2503.13470v1
• Date: Mon, 03 Mar 2025 16:16:38 GMT
• Title: Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment
• Authors: Mohammod N. I. Suvon, Shuo Zhou, Prasun C. Tripathi, Wenrui Fan, Samer Alabed, Bishesh Khanal, Venet Osmani, Andrew J. Swift, Chen, Chen, Haiping Lu,
• Abstract summary: Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMICs) due to the scarcity of advanced diagnostic tools.<n>We propose Lead-Specific Electrocardiogram Multimodal Variational Autoencoder (LS-EMVAE), a model pre-trained on large-population 12L-ECG data.<n>LS-EMVAE makes better predictions on both 12L-ECG and 6L-ECG at inference, making it an equitable solution for areas with limited or no diagnostic tools.
• Score: 71.69065905466567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMICs) primarily due to the scarcity of advanced diagnostic tools. Several studies in PH have applied machine learning to low-cost diagnostic tools like 12-lead ECG (12L-ECG), but they mainly focus on areas with limited resources, overlooking areas with no diagnostic tools, such as rural primary healthcare in LMICs. Recent studies have shown the effectiveness of 6-lead ECG (6L-ECG), as a cheaper and portable alternative in detecting various cardiac conditions, but its clinical value for PH detection is not well proved. Furthermore, existing methods treat 12L-/6L-ECG as a single modality, capturing only shared features while overlooking lead-specific features essential for identifying complex cardiac hemodynamic changes. In this paper, we propose Lead-Specific Electrocardiogram Multimodal Variational Autoencoder (LS-EMVAE), a model pre-trained on large-population 12L-ECG data and fine-tuned on task-specific data (12L-ECG or 6L-ECG). LS-EMVAE models each 12L-ECG lead as a separate modality and introduces a hierarchical expert composition using Mixture and Product of Experts for adaptive latent feature fusion between lead-specific and shared features. Unlike existing approaches, LS-EMVAE makes better predictions on both 12L-ECG and 6L-ECG at inference, making it an equitable solution for areas with limited or no diagnostic tools. We pre-trained LS-EMVAE on 800,000 publicly available 12L-ECG samples and fine-tuned it for two tasks: 1) PH detection and 2) phenotyping pre-/post-capillary PH, on in-house datasets of 892 and 691 subjects across 12L-ECG and 6L-ECG settings. Extensive experiments show that LS-EMVAE outperforms existing baselines in both ECG settings, while 6L-ECG achieves performance comparable to 12L-ECG, unlocking its potential for global PH screening in areas without diagnostic tools.

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