ecg

This paper introduces StenCE, a pretraining framework that uses cross-modal contrastive learning between ECG and X-ray angiography representations to detect severe coronary stenosis from ECGs, achieving high performance and enabling early diagnosis even in asymptomatic patients.

DeepArrhythmia is a multimodal framework for beat-level ECG arrhythmia classification that combines raw ECG signals and waveform images, using segment-level confidence to selectively acquire physiological evidence for improved accuracy.

MIT researchers have developed PULSE-HF, a deep learning model that predicts whether heart failure patients will experience worsening left ventricular ejection fraction within a year using electrocardiograms. The model, published in Lancet eClinical Medicine, could help clinicians prioritize high-risk patients and reduce unnecessary hospital visits in both well-resourced and low-resource clinical settings.