Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI

• URL: http://arxiv.org/abs/2601.00014v1
• Date: Sat, 20 Dec 2025 21:36:47 GMT
• Title: Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI
• Authors: Eran Zvuloni, Ronit Almog, Michael Glikson, Shany Brimer Biton, Ilan Green, Izhar Laufer, Offer Amir, Joachim A. Behar,
• Abstract summary: Heart failure affects 11.8% of adults aged 65 and older.<n>DeepHHF, trained on 24-hour ECG recordings, achieved an area under the receiver operating characteristic curve of 0.80.<n>High-risk individuals identified by DeepHHF had a two-fold chance of hospitalization or death incidents.
• Score: 1.0359164233869431
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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, with key attention between 8 AM and 3 PM. This study highlights the feasibility of deep learning to model 24-hour continuous ECG data, capturing paroxysmal events and circadian variations 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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