用24小时心电图数据预测五年内心衰风险，效果优于传统方法。
Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI
- 用深度学习分析整日单导联心电图，捕捉间歇性异常事件。
- 模型AUC达0.80，高危人群住院或死亡风险翻倍。
- 可解释性分析显示模型关注心律失常等关键异常，适合临床筛查。
心力衰竭（HF）影响65岁以上成年人的11.8%，显著降低生活质量与寿命。预防HF可减少发病率与死亡率。我们假设将人工智能应用于24小时单导联心电图（ECG）数据，可预测五年内心衰风险。研究使用泰赫尼昂-卢米特动态心电图（TLHE）数据集，包含47,729名患者共69,663条记录，历时20年采集。深度学习模型DeepHHF在24小时心电图上训练，获得0.80的受试者工作特征曲线下面积（AUC），优于基于30秒片段的模型和临床评分。DeepHHF识别出的高危人群住院或死亡风险翻倍。可解释性分析表明，模型重点关注心律失常和心脏异常。本研究证实，对连续24小时心电图建模具有可行性，能捕捉关键的阵发性事件，实现可靠风险预测。利用单导联动态心电图进行AI分析，具有无创、低成本、易获取优势，是心衰风险预测的有前景工具。
原文摘要 · Abstract (English)
Heart failure (HF) affects 11.8% of adults aged 65 and older, reducing quality of life and longevity. Preventing HF can reduce morbidity and mortality. We hypothesized that artificial intelligence (AI) applied to 24-hour single-lead electrocardiogram (ECG) data could predict the risk of HF within five years. To research this, the Technion-Leumit Holter ECG (TLHE) dataset, including 69,663 recordings from 47,729 patients, collected over 20 years was used. Our deep learning model, DeepHHF, trained on 24-hour ECG recordings, achieved an area under the receiver operating characteristic curve of 0.80 that outperformed a model using 30-second segments and a clinical score. High-risk individuals identified by DeepHHF had a two-fold chance of hospitalization or death incidents. Explainability analysis showed DeepHHF focused on arrhythmias and heart abnormalities. This study highlights the feasibility of deep learning to model 24-hour continuous ECG data, capturing paroxysmal events essential for reliable risk prediction. Artificial intelligence applied to single-lead Holter ECG is non-invasive, inexpensive, and widely accessible, making it a promising tool for HF risk prediction.
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