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Systematic comparison of ECG delineation algorithm performance on smartwatch data.
IEEE Access, DOI: 10.1109/ACCESS.2024.3487013 (2024)
Cardiovascular diseases are the leading cause of global mortality, necessitating early detection and continuous monitoring for timely interventions. Smartwatches with electrocardiogram (ECG) recording capabilities enable real-time, at-home cardiac monitoring. Specific ECG characteristics can provide insights into cardiovascular diseases. The delineation of ECGs, which is the identification of fiducial points (such as onsets, offsets, and peaks), is a time-consuming task. Automated ECG delineation can enhance this process, but existing research comparing available algorithms is limited. Furthermore, to the best of our knowledge, none have addressed single-lead ECGs from smartwatches, which can be noisy and unfiltered. Thus, this study evaluates the best-performing open-source algorithm for single-lead and smartwatch ECG data. We used two public datasets (Lobachevsky University Database, QT Database) and two smartwatch datasets (SmartHeartWatch Dataset, SMART Start Dataset) including two devices (AppleWatch, Withings ScanWatch). Algorithms from three toolkits (NeuroKit, ECGKit, ECGdeli) were assessed based on the time deviation between algorithm outputs and reference annotations, sensitivity, true positives, and false negatives. Results were further evaluated against the Common Standards for Quantitative Electrocardiography (CSE) recommendations. ECGdeli outperformed the other algorithms. For QRS on- and offset, ECGkit shows comparable sensitivity, but otherwise lower scores. NeuroKit consistently shows lower sensitivity across all four data sets, however, the temporal deviation between detected point and reference was higher. Overall, sensitivity scores were higher for Apple Watch data compared to Withings ScanWatch data. This study demonstrates that segmentation algorithms are applicable to single-lead smartwatch ECG data, with ECGdeli being the most stable overall, and NeuroKit recommended for scenarios prioritizing the temporal accuracy of detected points.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Cardiovascular Diseases ; Ecg Segmentation ; Mobile Health ; Wearable Devices
ISSN (print) / ISBN
2169-3536
e-ISSN
2169-3536
Zeitschrift
IEEE Access
Verlag
IEEE
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of AI for Health (AIH)