Scholarlink

Electrocardiograms (ECGs) record the heart’s electrical activity, offering valuable diagnostic and prognostic insights. We introduce Contrasting by Augmented Patient Electrocardiograms (CAPE), a foundation model trained using patient contrastive learning enhanced with temporal augmentations, on over six million unlabeled ECGs from diverse cohorts across three continents. We demonstrate CAPE’s effectiveness to generate a meaningful generic representation and validate its performance on an open-source external cohort, where it surpasses existing pretraining methods and fully supervised deep networks.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Contrasting by Augmented Patient Electrocardiograms to Learn Representations for a Foundation Model

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Gul Rukh Khattak,
  • Konstantinos Patlatzoglou,
  • Yixiu Liang,
  • Libor Pastika,
  • Boroumand Zeidaabadi,
  • Joseph Barker,
  • Mehak Gurnani,
  • Antonio H. Ribeiro,
  • Jeffrey Annis,
  • Antonio Luiz Pinho Ribeiro,
  • Nicholas Peters,
  • Junbo Ge,
  • Daniel B. Kramer,
  • Jonathan W. Waks,
  • Evan Brittain,
  • Arunashis Sau,
  • Fu Siong Ng

摘要

Electrocardiograms (ECGs) record the heart’s electrical activity, offering valuable diagnostic and prognostic insights. We introduce Contrasting by Augmented Patient Electrocardiograms (CAPE), a foundation model trained using patient contrastive learning enhanced with temporal augmentations, on over six million unlabeled ECGs from diverse cohorts across three continents. We demonstrate CAPE’s effectiveness to generate a meaningful generic representation and validate its performance on an open-source external cohort, where it surpasses existing pretraining methods and fully supervised deep networks.