Problem
Research questions and friction points this paper is trying to address.
heart failure
ECG
risk prediction
Holter monitoring
AI
Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI
🤖 AI Summary
This study proposes DeepHHF, a deep learning model that leverages 24-hour continuous single-lead Holter electrocardiogram (ECG) signals to predict the five-year risk of heart failure in older adults. Evaluated on a large-scale retrospective cohort of nearly 70,000 individuals, the model significantly outperforms conventional short-duration ECG approaches and established clinical risk scores, achieving an AUC of 0.80. DeepHHF also demonstrates interpretability by identifying key pathological features such as arrhythmias. Individuals classified as high-risk exhibited a two-fold increase in hospitalization or mortality rates, underscoring the clinical potential of combining long-duration ECG monitoring with artificial intelligence for non-invasive, low-cost early detection of heart failure.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
24-hour ECG
deep learning
explainable AI
heart failure prediction
Holter monitoring
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