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Impact of ECG dataset diversity on generalization of CNN model for detecting QRS complex

Habib, Ahsan, Karmakar, Chandan and Yearwood, John Leighton 2019, Impact of ECG dataset diversity on generalization of CNN model for detecting QRS complex, IEEE access, vol. 7, pp. 93275-93285, doi: 10.1109/ACCESS.2019.2927726.

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Title Impact of ECG dataset diversity on generalization of CNN model for detecting QRS complex
Author(s) Habib, Ahsan
Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Yearwood, John LeightonORCID iD for Yearwood, John Leighton orcid.org/0000-0002-7562-6767
Journal name IEEE access
Volume number 7
Start page 93275
End page 93285
Total pages 11
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2019
ISSN 2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Convolutional neural networks
Deep learning
ECG
Generalization
QRS complex
Supervised learning
Visual attention
Summary Detection of QRS complexes in electrocardiogram (ECG) signal is crucial for automated cardiac diagnosis. Automated QRS detection has been a research topic for over three decades and several of the traditional QRS detection methods show acceptable detection accuracy, however, the applicability of these methods beyond their study-specific databases was not explored. The non-stationary nature of ECG and signal variance of intra and inter-patient recordings impose significant challenges on single QRS detectors to achieve reasonable performance. In real life, a promising QRS detector may be expected to achieve acceptable accuracy over diverse ECG recordings and, thus, investigation of the model's generalization capability is crucial. This paper investigates the generalization capability of convolutional neural network (CNN) based-models from intra (subject wise leave-one-out and five-fold cross validation) and inter-database (training with single and multiple databases) points-of-view over three publicly available ECG databases, namely MIT-BIH Arrhythmia, INCART, and QT. Leave-one-out test accuracy reports 99.22%, 97.13%, and 96.25% for these databases accordingly and inter-database tests report more than 90% accuracy with the single exception of INCART. The performance variation reveals the fact that a CNN model's generalization capability does not increase simply by adding more training samples, rather the inclusion of samples from a diverse range of subjects is necessary for reasonable QRS detection accuracy.
Language eng
DOI 10.1109/ACCESS.2019.2927726
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, IEEE
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30128936

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.