{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:42:56Z","timestamp":1785544976938,"version":"3.56.0"},"reference-count":45,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Long-term garment-type wearable Holter electrocardiographic (ECG) monitoring is frequently affected by noise contamination, which complicates automated atrial fibrillation (AF) detection in real-world recordings. Although deep learning has shown high performance for AF detection, relatively few studies have evaluated explicit strategies for handling noise-included wearable ECG data. An alternative representation using the R-R interval (RRI) time series may reduce the dependence on waveform morphology and provide an alternative pathway for AF screening in noisy recordings.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aimed to develop and evaluate a 3-class, noise-aware RRI-based AF screening framework that explicitly separated AF, non-AF, and uninterpretable noise windows, and to assess the impact of analysis window length on model performance.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Single-lead garment-type wearable Holter ECG data from 117 patients at the University of Osaka Hospital were analyzed after exclusion of patients with documented atrial tachycardia, flutter, or paced rhythm according to the predefined task definition. R-peaks were automatically detected, and the resulting RRI segments were converted into 2D histogram images, with time on the x-axis and RRI-derived heart rate on the y-axis, for 1.5-, 3-, and 6-minute windows. A ResNet-34\u2013based 2D convolutional neural network was trained for 3-class classification. Model performance was evaluated using 5-fold interpatient cross-validation on the institutional dataset and independent external testing on the MIT-BIH (Massachusetts Institute of Technology\u2013Beth Israel Hospital) AF Database (AFDB). In the external validation, atrial flutter\u2013annotated intervals were excluded to match the training task definition. Patient-level AF burden was evaluated by comparing reference AF burden with model-estimated AF burden using Pearson and Spearman correlation coefficients, and linear regression.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Of 129 monitored patients between March 1, 2023, and November 20, 2025, 117 were analyzed. In the internal validation, the 3-class model (non-AF, AF, and noise) showed similarly high performance for the 1.5- and 3\n \n -minute windows, both with an accuracy of 96.6%. In independent external validation, the 3-minute window showed numerically the highest overall performance (accuracy: 97.3%; AF sensitivity: 96.9%; and AF specificity: 97.7%), although the differences across window lengths were modest. At the patient level, AF burden correlation was high across all window lengths, with Pearson\n r<\/jats:italic>\n of 0.995, 0.991, and 0.989 and Spearman \u03c1 of 0.988, 0.982, and 0.979 for the 1.5-, 3-, and 6-minute models, respectively.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The RRI-based 2D convolutional neural network achieved high AF classification accuracy and strong patient-level correlation with reference AF burden. Using RRI features and a 3-class framework, which explicitly separated noise from AF and non-AF rhythms, a 3-minute RRI window provided a favorable balance of performance for AF screening in a garment-type Holter ECG.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/91960","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:31:17Z","timestamp":1781616677000},"page":"e91960-e91960","source":"Crossref","is-referenced-by-count":0,"title":["R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation 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