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Updated: Jun 30, 2026
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Clinical performance of an atrial fibrillation burden tracking algorithm: Evaluation against reference public and
Arezoo Karimizadeh1,2, Amin Mahnam2, Soosan Beheshti1
1Electrical, Computer, and Biomedical Engineering Department, Toronto Metropolitan University, Toronto, Ontario, Canada.
Insights
A new textile ECG algorithm accurately detects atrial fibrillation (AF) and estimates its burden using machine learning. This wearable technology offers a practical solution for continuous AF monitoring outside the clinic.
Area of Science:
- Cardiology
- Biomedical Engineering
- Machine Learning
Background:
- Atrial fibrillation (AF) is the most common cardiac arrhythmia, increasing risks of stroke, heart failure, and mortality.
- Continuous monitoring is crucial for detecting paroxysmal or asymptomatic AF, which short-term ECGs often miss.
- Textile ECG platforms offer a promising solution for real-world rhythm assessment.
Purpose of the Study:
- To develop and validate a lightweight, interpretable algorithm for AF detection and burden estimation.
- Utilize textile ECG recordings acquired during daily life for continuous assessment.
- Provide a practical alternative to traditional monitoring devices.
Main Methods:
- Developed the Textile AF-Tracker (TAF-Tracker) algorithm using RR-interval-based machine learning.
- Incorporated entropy, Lorenz-plot, statistical, and fragmentation features with ECG quality metrics.
- Trained and validated multiple classifiers on public AF datasets and long-term textile ECG recordings.
Main Results:
- Achieved 96%-99% accuracy, sensitivity, and specificity across public datasets.
- XGBoost classifier demonstrated 98.4% accuracy in 14-day textile ECG recordings from 47 AF patients.
- AF burden estimation showed a median absolute error of 1.3%, with high specificity even during activity.
Conclusions:
- A lightweight, RR-interval-based machine learning algorithm on textile ECG accurately detects AF and quantifies burden.
- The TAF-Tracker algorithm combined with a textile platform provides a practical alternative for continuous AF surveillance.
- This approach facilitates improved management and treatment assessment for AF patients.
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% (interquartile range 0.7%-1.8%). In healthy and noise stress data, specificity remained ≥99%, even during activity.
Conclusion:
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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