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<p>Wearable devices enable electrocardiograms (ECGs) outside traditional healthcare settings. While these devices are usually equipped with single-lead ECGs to monitor rhythm abnormalities, recent studies show utility in detecting myocardial diseases when combined with artificial intelligence (AI). However, the optimal single-lead axis for detecting myocardial diseases beyond rhythm abnormality remains unclear. To address this knowledge gap, we trained models to detect 3 myocardial diseases: left ventricular systolic dysfunction (LVSD), hypertrophic cardiomyopathy (HCM), and cardiac amyloidosis (CA) using single-lead ECGs, systematically synthesized at 10-degree intervals, leveraging the limb leads from 12-lead ECG data. For all 3 diseases, the highest discriminations were observed near the aVR axis and its inverse. The optimal angles were 20° for LVSD (AUROC 0.884 [95% CI: 0.877–0.891]) and HCM (0.911 [0.900–0.922]), and 210° for CA (0.893 [0.888–0.899]). These findings may inform the development of wearable ECG devices with utility beyond rhythm abnormality detection.</p>

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Optimizing single-lead ECG axis for AI-based detection of myocardial diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Masamitsu Nakayama,
  • Ryuichiro Yagi,
  • Kyohei Daigo,
  • Yoshinori Katsumata,
  • Rahul C. Deo,
  • Calum A. MacRae,
  • Shinichi Goto

摘要

Wearable devices enable electrocardiograms (ECGs) outside traditional healthcare settings. While these devices are usually equipped with single-lead ECGs to monitor rhythm abnormalities, recent studies show utility in detecting myocardial diseases when combined with artificial intelligence (AI). However, the optimal single-lead axis for detecting myocardial diseases beyond rhythm abnormality remains unclear. To address this knowledge gap, we trained models to detect 3 myocardial diseases: left ventricular systolic dysfunction (LVSD), hypertrophic cardiomyopathy (HCM), and cardiac amyloidosis (CA) using single-lead ECGs, systematically synthesized at 10-degree intervals, leveraging the limb leads from 12-lead ECG data. For all 3 diseases, the highest discriminations were observed near the aVR axis and its inverse. The optimal angles were 20° for LVSD (AUROC 0.884 [95% CI: 0.877–0.891]) and HCM (0.911 [0.900–0.922]), and 210° for CA (0.893 [0.888–0.899]). These findings may inform the development of wearable ECG devices with utility beyond rhythm abnormality detection.