Key result
The Multi-Scale Self-Organized Operational Neural Network (MSSelf-ONN) model detected atrial fibrillation with 99.1% total accuracy and a 0.98 kappa coefficient, outperforming a standard CNN.
Why the study?
Existing deep learning methods for AF detection often feature complex structures or poor robustness due to reliance on linear neuron models, prompting the need for models with enhanced performance and computational efficiency.
Does the multi-scale Self-ONN model improve the detection of atrial fibrillation from ECG signals compared to conventional CNNs?
Does the multi-scale Self-ONN model improve the detection of atrial fibrillation from ECG signals compared to conventional CNNs?
The proposed multi-scale Self-ONN model demonstrates superior performance and computational efficiency in detecting atrial fibrillation from ECG signals compared to conventional CNNs.
Answers are grounded in this paper and cite their sources.
Should not yet change AF detection practice; leaves open real-world validation of MSSelf-ONN.
Atrial fibrillation is a common cardiac arrhythmia, and its incidence increases with age. Currently, numerous deep learning methods have been proposed for AF detection. However, these methods either have complex structures or poor robustness. Given the evidence from recent studies, it is not surprising to observe the limitations in the learning performance of these approaches. This can be attributed to their strictly homogenous conguration, which solely relies on the linear neuron model. The limitations mentioned above have been addressed by operational neural networks (ONNs). These networks employ a heterogeneous network configuration, incorporating neurons equipped with diverse nonlinear operators. Therefore, in this study, to enhance the detection performance while maintaining computational efficiency, a novel model named multi-scale Self-ONNs (MSSelf-ONNs) was proposed to identify AF. The proposed model possesses a significant advantage and superiority over conventional ONNs due to their self-organization capability. Unlike conventional ONNs, MSSelf -ONNs eliminate the need for prior operator search within the operator set library to find the optimal set of operators. This unique characteristic sets MSSelf -ONNs apart and enhances their overall performance. To validate and evaluate the system, we have implemented the experiments on the well-known MIT-BIH atrial fibrillation database. The proposed model yields total accuracies and kappa coefficients of 98% and 0.95, respectively. The experiment results demonstrate that the proposed model outperform the state-of-the-art deep CNN in terms of both performance and computational complexity.
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Zhang et al. (2023) studied Atrial Fibrillation (n=21). Multi-Scale Self-Organized Operational Neural Networks (MSSelf-ONNs) vs. Convolutional Neural Network (CNN-3) was evaluated on Total accuracy (TAC) for atrial fibrillation detection. The Multi-Scale Self-Organized Operational Neural Network (MSSelf-ONN) model detected atrial fibrillation with 99.1% total accuracy and a 0.98 kappa coefficient, outperforming a standard CNN.
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