Delta ECG: A Genetic Perspective
Track: Tiny Paper Track
Keywords: Contrastive Learning, Self-Supervised Learning, Unsupervised Learning
TL;DR: We present a novel metric of patient-specific cardiovascular state shifts, and preform GWAS and downstream analyses.
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
Attendance: Zachary Levine
Submission Number: 16
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