Delta ECG: A Genetic Perspective
Levine, Z Rossman, H Segal, E
Levine, Z
Rossman, H
Segal, E
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Department
Computational Biology
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Type
Workshop
Date
2025
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Language
English
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Abstract
The genetic basis of cardiovascular disease remains largely unresolved. Existing deep-learning applications to electrocardiograms (ECGs) focus on single phenotypes or isolated timepoints Radhakrishnan et al. (2023); Libiseller-Egger et al. (2022); Wang et al. (2023), introducing biases. Here, we present Delta ECG, a metric based on pairs of embeddings that captures patient-specific changes in cardiovascular state over time. We demonstrate that this measure reliably differentiates within-patient variation from population-level differences and aligns with genome-wide significant loci for cardiovascular disease (CVD) and its risk factors
Citation
Z. Levine, H. Rossman, and E. Segal, “Delta ECG: A Genetic Perspective,” Mar. 06, 2025.
Source
Learning Meaningful Representations of Life (LMRL) Workshop at ICLR 2025
Conference
Keywords
Contrastive Learning, Self-Supervised Learning, Unsupervised Learning
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OpenReview