{"ID":2826839,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00014","arxiv_id":"2601.00014","title":"Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI","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.","short_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 researc...","url_abs":"https://arxiv.org/abs/2601.00014","url_pdf":"https://arxiv.org/pdf/2601.00014v1","authors":"[\"Eran Zvuloni\",\"Ronit Almog\",\"Michael Glikson\",\"Shany Brimer Biton\",\"Ilan Green\",\"Izhar Laufer\",\"Offer Amir\",\"Joachim A. Behar\"]","published":"2025-12-20T21:36:47Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}