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Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening

Halvaei, Hesam LU ; Hygrell, Tove ; Svennberg, Emma ; Corino, Valentina ; Sornmo, Leif LU and Stridh, Martin LU (2024) In IEEE Journal of Translational Engineering in Health and Medicine
Abstract

Objective: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT. Methods: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify... (More)

Objective: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT. Methods: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable. Results: The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. Conclusion: The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector. Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Atrial fibrillation screening, Databases, Detectors, Electrocardiography, handheld ECG device, Morphology, Noise level, non-sustained supraventricular tachycardia, Recording, Rhythm, signal quality
in
IEEE Journal of Translational Engineering in Health and Medicine
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85192998421
ISSN
2168-2372
DOI
10.1109/JTEHM.2024.3397739
language
English
LU publication?
yes
id
6b2448ec-5c1f-46f6-8ddb-f8aa5a21a84c
date added to LUP
2024-05-28 13:46:29
date last changed
2024-05-28 13:47:09
@article{6b2448ec-5c1f-46f6-8ddb-f8aa5a21a84c,
  abstract     = {{<p>Objective: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT. Methods: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable. Results: The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. Conclusion: The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector. Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.</p>}},
  author       = {{Halvaei, Hesam and Hygrell, Tove and Svennberg, Emma and Corino, Valentina and Sornmo, Leif and Stridh, Martin}},
  issn         = {{2168-2372}},
  keywords     = {{Atrial fibrillation screening; Databases; Detectors; Electrocardiography; handheld ECG device; Morphology; Noise level; non-sustained supraventricular tachycardia; Recording; Rhythm; signal quality}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Journal of Translational Engineering in Health and Medicine}},
  title        = {{Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening}},
  url          = {{http://dx.doi.org/10.1109/JTEHM.2024.3397739}},
  doi          = {{10.1109/JTEHM.2024.3397739}},
  year         = {{2024}},
}