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AuthorZahid, Muhammad Uzair
AuthorKiranyaz, Serkan
AuthorGabbouj, Moncef
Available date2023-09-24T08:57:19Z
Publication Date2023
Publication NameIEEE Transactions on Biomedical Engineering
ResourceScopus
ISSN1558-2531
URIhttp://dx.doi.org/10.1109/TBME.2022.3187874
URIhttp://hdl.handle.net/10576/47895
AbstractObjective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performance. Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs). Significance: As a pioneer application, the results show that compact and shallow 1D Self-ONNs with the feature injection can surpass all state-of-the-art deep models with a significant margin and with minimal computational complexity. Conclusion: This study has demonstrated that using a compact and superior network model, a global ECG classification can still be achieved with an elegant performance level even when no patient-specific information is used.
Languageen
PublisherIEEE Computer Society
Subjectgenerative neurons
Inter-patient ECG classification
operational neural networks
real-time heart monitoring
TitleGlobal ECG Classification by Self-Operational Neural Networks with Feature Injection
TypeArticle
Pagination205-215
Issue Number1
Volume Number70
dc.accessType Open Access


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