| Title: |
Short-term atrial fibrillation onset prediction using machine learning |
| Authors: |
Grégoire, Jean-Marie; Gilon, Cédric; Marelli, François; Bersini, Hugues; Groben, Laurent; Nguyen, Thomas; Deruyter, Bernard; Godart, Pascal; Carlier, Stéphane |
| Source: |
European heart journal. Digital health, 6 (6 |
| Publisher Information: |
2025-11-01 |
| Document Type: |
Electronic Resource |
| Abstract: |
Introduction Integrating machine learning (ML) models into wearable or connected devices to deliver early warning alerts prior to atrial fibrillation (AF) onset may represent an effective preventive strategy. Machine learning algorithms applied to two-lead Holter electrocardiogram (ECG) recordings can support the development of predictive models capable of detecting imminent paroxysmal AF episodes within short-term windows. This approach could facilitate a more targeted 'pill-in-the-pocket' (PITP)-like intervention strategy, potentially enhancing timely therapeutic actions and improving patient outcomes. Aim This study aimed to identify patients currently in sinus rhythm who will experience an AF episode within the subsequent hours by analysing 24-h Holter ECG recordings with ML. Methods We established a novel database comprising 95 871 manually analysed Holter ECG recordings, identifying 1319 episodes of paroxysmal AF from 872 patients. Among these, 835 AF episodes from 506 recordings had more than 60 min of normal sinus rhythm prior to AF onset and more than 10 min of sustained AF following onset. Patients were stratified into five age groups: all patients combined, under 60 years, 60-70 years, 70-80 years, and over 80 years. Additionally, 365 recordings from 347 patients without rhythm abnormalities were identified and classified, from which two ECG segments were selected. Two deep learning (DL) models were trained on raw ECG data to predict AF onset. To compare DL models with traditional ML approaches using heart rate variability (HRV) parameters, we employed a random forest classifier and a gradient-boosted decision tree model (XGBoost, XGB). Results The decision trees models trained on HRV parameters delivered the best predictive performance. The most significant results were observed for episodes lasting more than 5 min of AF, achieving an area under the receiver operating characteristic curve of 0.919 (95% CI: 0.879-0.958) and an area under the precision-rec; SCOPUS: ar.j; info:eu-repo/semantics/published |
| Index Terms: |
Cardiologie et circulation; Atrial fibrillation; Autonomic nervous system; Deep learning; Heart rate variability; Identification; Machine learning; Prediction; info:eu-repo/semantics/article; info:ulb-repo/semantics/articlePeerReview; info:ulb-repo/semantics/openurl/article |
| URL: |
https://dipot.ulb.ac.be/dspace/bitstream/2013/402256/3/gregoire2025.pdf; http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/402256; https://worldcat.org/search?q=on:EQY+http://difusion-oai.ulb.ac.be/oai/request+DCG_ENTIRE_REPOSITORY+CNTCOLL |
| Availability: |
Open access content. Open access content; 1 full-text file(s): info:eu-repo/semantics/openAccess |
| Note: |
1 full-text file(s): application/pdf; English |
| Other Numbers: |
EQY oai:dipot.ulb.ac.be:2013/402256; uri/info:doi/10.1093/ehjdh/ztaf104; uri/info:scp/105022498521; 1576239237 |
| Contributing Source: |
UNIVERSITE LIBRE DE BRUXELLES; From OAIster®, provided by the OCLC Cooperative. |
| Accession Number: |
edsoai.on1576239237 |
| Database: |
OAIster |