Detection of Short Supraventricular Tachycardias in Single-lead ECGs Recorded Using a Handheld Device

Hesam Halvaei, Tove Hygrell, Emma Svennberg, Valentina D.A. Corino, Leif Sornmo, Martin Stridh

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review


Short supraventricular tachycardias (S-SVTs) have been associated with a higher risk of developing atrial fibrillation (AF). Hence, identification of participants with such arrhythmias may increase the yield of AF screening. However, the lower signal quality of ECGs recorded using handheld screening devices challenges the detection of S-SVT. In the present work, a new method for detection of S-SVT is presented, which is based on the requirement on morphologic similarity between the detected beats. Specifically, any episode with a sequence of beats of similar morphology is considered as an S-SVT candidate while any episode with detections of different morphology, either due to signal disturbances or aberrant ectopic beats, is excluded. For this purpose, a support vector machine (SVM) was trained and validated, using a simulated ECG database, to classify an episode as either consisting of beats of similar or non-similar morphologies. Episodes identified as S-SVT candidates are subject to two further rhythm criteria in order to confirm the presence of an S-SVT. The performance of the S-SVT detector is evaluated using a subset of the StrokeStop I database (305 S-SVT out of 8258), resulting in a sensitivity, specificity, and positive predictive value of 88.8%, 92.0%, and 29.9%, respectively. In conclusion, the results suggest that the detection of S-SVT in AF screening can be done at an acceptable balance between sensitivity and positive predictive value.

Original languageEnglish
Title of host publicationComputing in Cardiology, CinC 2022
PublisherIEEE Computer Society
ISBN (Electronic)9798350300970
Publication statusPublished - 2022
Event2022 Computing in Cardiology, CinC 2022 - Tampere, Finland
Duration: 2022 Sept 42022 Sept 7


Conference2022 Computing in Cardiology, CinC 2022

Subject classification (UKÄ)

  • Cardiac and Cardiovascular Systems


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