Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 11; pp. 9363 - 9374
• Main Authors: Gabbouj, Moncef, Kiranyaz, Serkan, Malik, Junaid, Zahid, Muhammad Uzair, Ince, Turker, Chowdhury, Muhammad E. H., Khandakar, Amith, Tahir, Anas
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-11-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Benchmark testing; Biomedical monitoring; Complexity; Computational neuroscience; Configurations; Convolutional neural networks (CNNs); Datasets; EKG; Electrocardiography; Holter monitors; Libraries; Monitoring; Monitors; Neural networks; Neurogenesis; Neurons; operational neural networks (ONNs); R-peak detection; Signal quality; Training
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2022.3158867

Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors. Recently, this issue has been addressed by deep 1-D convolutional neural networks (CNNs) that have achieved state-of-the-art performance levels in Holter monitors; however, they pose a high complexity level that requires special parallelized hardware setup for real-time processing. On the other hand, their performance deteriorates when a compact network configuration is used instead. This is an expected outcome as recent studies have demonstrated that the learning performance of CNNs is limited due to their strictly homogenous configuration with the sole linear neuron model. This has been addressed by operational neural networks (ONNs) with their heterogenous network configuration encapsulating neurons with various nonlinear operators. In this study, to further boost the peak detection performance along with an elegant computational efficiency, we propose 1-D Self-Organized ONNs (Self-ONNs) with generative neurons. The most crucial advantage of 1-D Self-ONNs over the ONNs is their self-organization capability that voids the need to search for the best operator set per neuron since each generative neuron has the ability to create the optimal operator during training. The experimental results over the China Physiological Signal Challenge-2020 (CPSC) dataset with more than one million ECG beats show that the proposed 1-D Self-ONNs can significantly surpass the state-of-the-art deep CNN with less computational complexity. Results demonstrate that the proposed solution achieves a 99.10% F1-score, 99.79% sensitivity, and 98.42% positive predictivity in the CPSC dataset, which is the best R-peak detection performance ever achieved.