Key result
A proposed shallow 1D Convolutional Neural Network model achieved an accuracy of 97.40% in detecting arrhythmias from ECG signals.
Why the study?
Traditional machine learning algorithms for ECG-based arrhythmia detection require manual feature extraction, unlike deep learning techniques that learn parameters simultaneously.
A shallow 1D CNN model can achieve high accuracy (97.4%) in detecting arrhythmias from ECG signals, offering a computationally simpler alternative to deep neural networks.
Recent advances in biomedical applications have focused a lot of emphasis on the detection of which could be caused by cardiovascular disease (CVD) The electrocardiogram (ECG), which depicts the electrical activity of the heart, is the foundation for arrhythmia analysis. Different machine learning methods used on ECG datasets have demonstrated excellent performance in detecting arrhythmias. Nevertheless, feature extraction is necessary for machine learning algorithms. Modern deep learning techniques don't require feature extraction because they learn all the parameters simultaneously, in contrast to these techniques. In this study, a 1D CNN approach is presented and tested on the arrhythmia database of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH). The proposed model, which only has three layers, attained an accuracy of 97.40%.
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tara rasti (2023) studied Arrhythmia. Shallow 1D Convolutional Neural Network (CNN) was evaluated on Classification accuracy for normal vs abnormal heartbeats. A proposed shallow 1D Convolutional Neural Network model achieved an accuracy of 97.40% in detecting arrhythmias from ECG signals.
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