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المؤلفZahid, Muhammad Uzair
المؤلفKiranyaz, Serkan
المؤلفGabbouj, Moncef
تاريخ الإتاحة2023-09-24T08:57:19Z
تاريخ النشر2023
اسم المنشورIEEE Transactions on Biomedical Engineering
المصدرScopus
الرقم المعياري الدولي للكتاب1558-2531
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TBME.2022.3187874
معرّف المصادر الموحدhttp://hdl.handle.net/10576/47895
الملخصObjective: 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.
اللغةen
الناشرIEEE Computer Society
الموضوعgenerative neurons
Inter-patient ECG classification
operational neural networks
real-time heart monitoring
العنوانGlobal ECG Classification by Self-Operational Neural Networks with Feature Injection
النوعArticle
الصفحات205-215
رقم العدد1
رقم المجلد70
dc.accessType Open Access


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