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

Halvaei, Hesam LU ; Hygrell, Tove ; Svennberg, Emma ; Corino, Valentina D.A. ; Sornmo, Leif LU and Stridh, Martin LU (2022) 2022 Computing in Cardiology, CinC 2022
Abstract

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.... (More)

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.

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author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Computing in Cardiology, CinC 2022
publisher
IEEE Computer Society
conference name
2022 Computing in Cardiology, CinC 2022
conference location
Tampere, Finland
conference dates
2022-09-04 - 2022-09-07
external identifiers
  • scopus:85152956994
ISBN
9798350300970
DOI
10.22489/CinC.2022.314
language
English
LU publication?
yes
id
8f63bd13-3cbc-4560-b768-0c9df59524a4
date added to LUP
2023-08-15 10:45:01
date last changed
2024-02-19 22:31:13
@inproceedings{8f63bd13-3cbc-4560-b768-0c9df59524a4,
  abstract     = {{<p>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.</p>}},
  author       = {{Halvaei, Hesam and Hygrell, Tove and Svennberg, Emma and Corino, Valentina D.A. and Sornmo, Leif and Stridh, Martin}},
  booktitle    = {{Computing in Cardiology, CinC 2022}},
  isbn         = {{9798350300970}},
  language     = {{eng}},
  publisher    = {{IEEE Computer Society}},
  title        = {{Detection of Short Supraventricular Tachycardias in Single-lead ECGs Recorded Using a Handheld Device}},
  url          = {{http://dx.doi.org/10.22489/CinC.2022.314}},
  doi          = {{10.22489/CinC.2022.314}},
  year         = {{2022}},
}