The authors pre-trained an ECG encoder using contrastive learning that pairs 12-lead waveforms with multiple clinical note types, rather than only machine-generated ECG interpretation reports.
Pairing ECGs with multiple clinical report types yields a more universal ECG foundation model
MS-ECG-FM, pre-trained with multi-source contrastive learning, outperforms prior ECG foundation models across a wider span of detection benchmarks, including reduced-lead settings.
Big Tech
Robert A. Lewis · I-Min Chiu · Kyle Verrier · Karthik Jayaraman Raghuram · Francoise Marvel · Salar Abbaspourazad · +4 more
Massachusetts Institute of Technology · Apple, Inc · Johns Hopkins Medicine · Princeton University
Research Digest··2 min read
The authors introduce MS-ECG-FM, an ECG foundation model trained by contrastively aligning waveforms to ECG, echocardiography, radiology, and discharge reports.
Why this paper
From Apple, Inc and 3 others
In one line
Training on multiple clinical note types improves ECG foundation model performance beyond using only ECG reports.
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