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Considerations on Performance Evaluation of Atrial Fibrillation Detectors

Butkuviene, Monika ; Petrenas, Andrius ; Solosenko, Andrius ; Martin-Yebra, Alba ; Marozas, Vaidotas and Sornmo, Leif LU (2021) In IEEE Transactions on Biomedical Engineering
Abstract

Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to... (More)

Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. Conclusion: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.

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organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Annotations, Atrial fibrillation, Databases, deep learning, detection, Detectors, Electrocardiography, expert-crafted detection, Monitoring, performance evaluation, performance measures, Rhythm
in
IEEE Transactions on Biomedical Engineering
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • pmid:33750686
  • scopus:85103252943
ISSN
0018-9294
DOI
10.1109/TBME.2021.3067698
language
English
LU publication?
yes
id
57e7942a-14d7-4c5c-943f-4307fbab3430
date added to LUP
2021-04-08 14:34:05
date last changed
2021-04-09 03:00:04
@article{57e7942a-14d7-4c5c-943f-4307fbab3430,
  abstract     = {<p>Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. Conclusion: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.</p>},
  author       = {Butkuviene, Monika and Petrenas, Andrius and Solosenko, Andrius and Martin-Yebra, Alba and Marozas, Vaidotas and Sornmo, Leif},
  issn         = {0018-9294},
  language     = {eng},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Biomedical Engineering},
  title        = {Considerations on Performance Evaluation of Atrial Fibrillation Detectors},
  url          = {http://dx.doi.org/10.1109/TBME.2021.3067698},
  doi          = {10.1109/TBME.2021.3067698},
  year         = {2021},
}