Why the study?
Convolutional neural networks have been used to analyze electrocardiography data, but the integration of the Taguchi method to classify single-heartbeat ECG images without feature extraction or signal conversion remained to be explored.
Does combining the Taguchi method and Convolutional Neural Networks (CNNs) accurately classify arrhythmias using ECG images from single heartbeats?
Does combining the Taguchi method and Convolutional Neural Networks (CNNs) accurately classify arrhythmias using ECG images from single heartbeats?
A novel machine learning model combining the Taguchi method and CNNs achieved 96.79% accuracy in classifying arrhythmias from single heartbeat ECG images.
Supports hybrid ML models for ECG arrhythmia detection; leaves open prospective clinical validation.
In recent years, deep learning has been applied in numerous fields and has yielded excellent results. Convolutional neural networks (CNNs) have been used to analyze electrocardiography (ECG) data in biomedical engineering. This study combines the Taguchi method and CNNs for classifying ECG images from single heartbeats without feature extraction or signal conversion. All of the fifteen types (five classes) in the MIT-BIH Arrhythmia Dataset were included in this study. The classification accuracy achieved 96.79%, which is comparable to the state-of-the-art literature. The proposed model demonstrates effective and efficient performance in the identification of heartbeat diseases while minimizing misdiagnosis.
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Li et al. (2023) studied this question.
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