Key result
XGBoost algorithm integrated with textile ECG achieves ~98% accuracy for AF detection over 14 days.
Why the study?
Short-term ECG monitoring often misses paroxysmal or asymptomatic AF, highlighting the need for textile ECG platforms for continuous real-world rhythm assessment.
Does the Textile AF-Tracker machine learning algorithm accurately detect atrial fibrillation and estimate AF burden using continuous textile ECG recordings?
Does the Textile AF-Tracker machine learning algorithm accurately detect atrial fibrillation and estimate AF burden using continuous textile ECG recordings?
A machine learning algorithm applied to a wearable textile ECG accurately detects atrial fibrillation and quantifies AF burden over 14 days, offering a viable alternative to traditional Holter monitors.
Answers are grounded in this paper and cite their sources.
May provide comfortable Holter alternative for long-term AF monitoring; extends evidence for ML on textile ECG.
Abstract Background Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, increases risks of stroke, heart failure, and mortality. Short-term electrocardiographic (ECG) monitoring often misses paroxysmal or asymptomatic AF, underscoring the value of textile ECG platforms for continuous real-world rhythm assessment. Objective To develop and clinically validate a lightweight, interpretable algorithm for AF detection and burden estimation using textile ECG recordings acquired during daily life. Methods We developed Textile AF-Tracker (TAF-Tracker), an RR-interval-based machine learning pipeline using entropy, Lorenz-plot, statistical, and fragmentation features with ECG quality metrics to detect AF in 60-beat segments. Multiple classifiers (Random Forest, XGBoost, Support Vector Machine, logistic regression, and a threshold-based method) were trained on public AF datasets and long-term 3-lead textile ECG recordings (SKIIN™). Data were split by subject into training, validation, and test sets to ensure unseen data were tested. Results Across public long-term AF datasets, Random Forest and XGBoost achieved 96%-99% accuracy, 95%-99% sensitivity, and 96%-99% specificity. In 14-day textile ECG recordings from 47 AF patients, XGBoost reached 98.4% accuracy (sensitivity 96.1%, specificity 98.7%). AF burden showed a median absolute error of 1.3% (IQR 0.7%-1.8%). In healthy and noise-stress data, specificity remained ≥99%, even during activity. Conclusions A lightweight RR‐interval-based machine learning algorithm on textile ECG accurately detects AF and quantifies burden in long-term recordings with minimal error. Combined with a comfortable multi-lead textile platform, it provides a practical alternative to Holter monitors and implantable devices for continuous AF surveillance and treatment assessment.
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Karimizadeh et al. (2026) studied Adults with known atrial fibrillation undergoing 14-day continuous monitoring using textile ECG chestband (n=47). XGBoost machine learning algorithm for AF detection integrated with SKIIN textile ECG platform vs. Random Forest algorithm and other machine learning models was evaluated on Segment-based atrial fibrillation detection accuracy, sensitivity, specificity, and AF burden estimation error (%) on 14-day textile ECG recordings (Accuracy 98.4% (XGBoost) vs 97.6% (Random Forest)). XGBoost algorithm integrated with textile ECG achieved 98.4% accuracy, 96.1% sensitivity, 98.7% specificity, and median absolute AF burden estimation error of 1.3% over 14-day monitoring in adults with atrial fibrillation.
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