Clinical Validation of Wearable Arrhythmia Detection

This clinical methods review examines clinical validation of wearable arrhythmia detection. The organizing question is how diagnostic accuracy, confirmatory testing, adherence, and downstream clinical actions should be evaluated together. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to equating signal-detection accuracy with improved patient outcomes. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in screening and longitudinal rhythm monitoring.

1. Alfaras, M., Soriano, M. C., & Ortín, S. (2019). A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection. Frontiers in Physics, 7. https://doi.org/10.3389/fphy.2019.00103 DOI
2. Ansari, Y., Mourad, O., Qaraqe, K., & Serpedin, E. (2023). Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023. Frontiers in Physiology, 14, 1246746. https://doi.org/10.3389/fphys.2023.1246746 DOI
3. Huang, J., Chen, B., Yao, B., & He, W. (2019). ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network. IEEE Access, 7, 92871-92880. https://doi.org/10.1109/access.2019.2928017 DOI
4. Lown, M., Brown, M., Brown, C., Yue, A. M., Shah, B. N., Corbett, S. J., Lewith, G., Stuart, B., Moore, M., & Little, P. (2020). Machine learning detection of Atrial Fibrillation using wearable technology. PLoS ONE, 15(1), e0227401. https://doi.org/10.1371/journal.pone.0227401 DOI
5. Lubitz, S. A., Faranesh, A. Z., Selvaggi, C., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., McConnell, M. V., Pantelopoulos, A., & Foulkes, A. S. (2022). Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study. Circulation, 146(19), 1415-1424. https://doi.org/10.1161/circulationaha.122.060291 DOI
6. Niu, Y., Wang, H., Wang, H., Zhang, H., Jin, Z., & Guo, Y. (2023). Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection. Digital Health, 9, 20552076231198682. https://doi.org/10.1177/20552076231198682 DOI
7. Sadrawi, M., Lin, C. H., Lin, Y. T., Hsieh, Y., Kuo, C. C., Chien, J., Haraikawa, K., Abbod, M., & Shieh, J. S. (2017). Arrhythmia Evaluation in Wearable ECG Devices. Sensors, 17(11), 2445. https://doi.org/10.3390/s17112445 DOI
8. Woort, F. T., Dubois, G., Tansley, G., Didier, M., Verdegaal, E. L., Franklin, S., & Erck‐Westergren, E. V. (2022). Validation of an equine fitness tracker: ECG quality and arrhythmia detection. Equine Veterinary Journal, 55(2), 336-343. https://doi.org/10.1111/evj.13565 DOI
9. Yang, C., Veiga, C., Rodriguez-Andina, J. J., Farina, J., Iniguez, A., & Yin, S. (2019). Using PPG Signals and Wearable Devices for Atrial Fibrillation Screening. IEEE Transactions on Industrial Electronics, 66(11), 8832-8842. https://doi.org/10.1109/tie.2018.2889614 DOI
10. Yao, Q., Wang, R., Fan, X., Liu, J., & Li, Y. (2019). Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network. Information Fusion, 53, 174-182. https://doi.org/10.1016/j.inffus.2019.06.024 DOI