Artificial Intelligence‐Enabled Short‐Term Ambulatory Monitoring ECG During Sinus Rhythm for Prediction of Hidden Atrial Fibrillation.
Background: Screening of asymptomatic/occult atrial fibrillation (AF) remains challenging. This study aimed to use a deep learning model to predict hidden AF in patients who showed normal sinus rhythm (SR) during 24‐h Holter monitoring. Methods: This was a retrospective cohort study that enrolled 93...
| Publicado en: | Journal of Cardiovascular Electrophysiology Vol. 36; no. 11; pp. 2808 - 2816 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research tables/charts tracings Journal Article |
| Publicado: |
Wiley-Blackwell
Nov2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190789791&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190789791 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10453873 GSB jtl: Journal of Cardiovascular Electrophysiology issn: 10453873 maglogo: Y pubinfo: dt: Nov2025 vid: 36 iid: 11 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 190789791 186859400 190789791 190789791 10.1111/jce.70028 190789791 ppf: 2808 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Artificial Intelligence‐Enabled Short‐Term Ambulatory Monitoring ECG During Sinus Rhythm for Prediction of Hidden Atrial Fibrillation. aug: au: Chang, Ting‐Yung Chen, Song‐Po Hsieh, Jui‐Hung Huang, Guan‐Jun Hung, King‐Chu Guo, Shu‐Mei Liu, Chih‐Min Chang, Shih‐Lin Lin, Yenn‐Jiang Lo, Li‐Wei Hu, Yu‐Feng Chung, Fa‐Po Lin, Chin‐Yu Chen, Shih‐Ann affil: Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan sug: subj: Electrocardiography, Ambulatory Atrial Fibrillation Risk Factors Prediction Models Deep Learning Risk Assessment Heart Rate Signal Processing, Computer Assisted Human Retrospective Design Record Review Prospective Studies Long Short-Term Memory Sensitivity and Specificity Hospitals Taiwan Taiwan Atrial Fibrillation Diagnosis Male Female Adult Middle Age Aged Comorbidity Hypertension Coronary Arteriosclerosis Diabetes Mellitus Renal Insufficiency, Chronic Thyroid Diseases Stroke Pulmonary Disease, Chronic Obstructive ROC Curve Descriptive Statistics Data Analysis Software Funding Source Atrial Fibrillation Physiopathology Predictive Value of Tests Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Background: Screening of asymptomatic/occult atrial fibrillation (AF) remains challenging. This study aimed to use a deep learning model to predict hidden AF in patients who showed normal sinus rhythm (SR) during 24‐h Holter monitoring. Methods: This was a retrospective cohort study that enrolled 934 patients receiving 24‐h ambulatory Holter monitoring. Of them, 640 patients (AF group) had the documented paroxysmal AF in the index Holter monitoring. The rest of 294 patients (Control group) did not have medical record of AF and the index Holter exam did not detect AF. A ConvNeXt model (1st stage) and Long Short‐Term Memory (LSTM) (2nd stage) was used to predict the probability of AF. Results: 368,550 eligible SR ECG segments (60 s/segment) were taken into 1st staged classification, and the Area Under Curve (AUC) was 0.7755 with accuracy of 0.7755, sensitivity of 0.9105, and specificity of 0.5718. After 2nd staged classification (10‐min SR ECG recording), the AUC reached 0.874 (accuracy of 0.8213, sensitivity of 0.8339, and specificity of 0.8115). The fact that longer time length examined in the 2nd stage leaded to a dilution of features more related to AF might decrease specificity compared with 1st stage. Subgroup analysis demonstrated that night‐time settings had better performance (AUC: 0.902 in night‐time, 0.8726 in day‐time). Conclusion: We developed an AI‐enabled model with 10 min ECG recording from ambulatory Holter monitoring during SR to predict hidden AF with high accuracy. Subgroup analysis according to the diurnal period, night‐time settings showed more favorable performance compared to day‐time recordings. pubtype: Academic Journal doctype: research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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