Scholarlink

Introduction <p>Detecting paroxysmal atrial fibrillation (pAF) from sinus rhythm could enable earlier intervention and stroke prevention. We developed a deep-learning Holter electrocardiograph (ECG) algorithm and prospectively evaluated its patient-level performance against 7-day AF outcomes.</p> Methods <p>We curated 20,000 30-s sinus rhythm blocks (125&#xa0;Hz) from Holter ECG data of patients with and without pAF, trained convolutional models with tenfold cross-validation, and assessed a separate validation set (<i>n</i> = 54; 27 pAF, 27 controls) to select the operating threshold. A multicenter prospective study then evaluated the algorithm using ten consecutive 30-s sinus rhythm blocks per patient with a 4/10 positive rule; patients with pAF underwent concurrent 7-day patch monitoring to anchor outcomes.</p> Results <p>Cross-validation during development yielded mean sensitivity 84.2% and specificity 66.2%; the best tuned model achieved 84.9% sensitivity and 69.9% specificity on the separate set. In the clinical trial, among 24 patients with AF documented within 7&#xa0;days and 20 controls, the device showed sensitivity 91.7% (95% confidence interval (CI) 73.0–99.0) and specificity 65.0% (40.8–84.6). No device-related adverse events occurred.</p> Conclusion <p>An artificial intelligence (AI) analyzing short sinus rhythm Holter segments can identify patients who develop pAF within 7&#xa0;days, supporting use as a triage tool for intensified rhythm monitoring.</p> Trial Registration <p>UMIN-CTR UMIN000047182.</p>

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

Artificial Intelligence Software for Detecting Paroxysmal Atrial Fibrillation from Sinus Rhythm Monitor ECG: Development and Clinical Trial

  • 期刊论文

  • 发表日期：

    2025年12月27日

查看全文
  • Yuichi Tamura,
  • Tomohiro Takata,
  • Hirohisa Taniguchi,
  • Ryo Takemura,
  • Mineki Takechi,
  • Rika Takeyasu,
  • Eiichi Watanabe,
  • Hirotaka Yada,
  • Yudai Tamura,
  • Jin Iwasawa,
  • Tadahiro Taniguchi,
  • Satoshi Ogawa

摘要

Introduction

Detecting paroxysmal atrial fibrillation (pAF) from sinus rhythm could enable earlier intervention and stroke prevention. We developed a deep-learning Holter electrocardiograph (ECG) algorithm and prospectively evaluated its patient-level performance against 7-day AF outcomes.

Methods

We curated 20,000 30-s sinus rhythm blocks (125 Hz) from Holter ECG data of patients with and without pAF, trained convolutional models with tenfold cross-validation, and assessed a separate validation set (n = 54; 27 pAF, 27 controls) to select the operating threshold. A multicenter prospective study then evaluated the algorithm using ten consecutive 30-s sinus rhythm blocks per patient with a 4/10 positive rule; patients with pAF underwent concurrent 7-day patch monitoring to anchor outcomes.

Results

Cross-validation during development yielded mean sensitivity 84.2% and specificity 66.2%; the best tuned model achieved 84.9% sensitivity and 69.9% specificity on the separate set. In the clinical trial, among 24 patients with AF documented within 7 days and 20 controls, the device showed sensitivity 91.7% (95% confidence interval (CI) 73.0–99.0) and specificity 65.0% (40.8–84.6). No device-related adverse events occurred.

Conclusion

An artificial intelligence (AI) analyzing short sinus rhythm Holter segments can identify patients who develop pAF within 7 days, supporting use as a triage tool for intensified rhythm monitoring.

Trial Registration

UMIN-CTR UMIN000047182.