Key result
A hybrid CNN+LSTM model with three-step preprocessing outperformed contemporary models in automating arrhythmia categorization on denoised ECG signals.
Why the study?
Using denoised ECG signals instead of raw signals increases the probability of better arrhythmia identification, prompting improved preprocessing and automated categorization methods.
Does a hybrid CNN+LSTM model with rigorous preprocessing improve arrhythmia classification accuracy on ECG signals compared to contemporary models?
Does a hybrid CNN+LSTM model with rigorous preprocessing improve arrhythmia classification accuracy on ECG signals compared to contemporary models?
A hybrid CNN+LSTM deep learning model with rigorous preprocessing improves the classification accuracy of arrhythmias from ECG signals.
May aid automated arrhythmia detection on ECG; leaves open prospective clinical validation before practice adoption.
Proactive illness diagnosis with AI and related technologies has been an intriguing and productive field in the last ten years. Cardiovascular illnesses are among the medical conditions that need regular monitoring. Arrhythmia, a type of coronary heart disease, is frequently observed by clinicians using electrocardiography (ECG). In humans, an ECG records electrical activity and cardiac rhythm. In recent decades, there has been a substantial surge in the use of neural networks to detect cardiovascular abnormalities. It has been shown that using the denoised signal as compared to the raw input signal increases the probability of better identification of arrhythmias. In this paper, rigorous, three-step preprocessing is done to improve classification accuracy. Firstly, denoising is done using a wavelet transform, then, for baseline artifact filtering, five filters have been applied to ECG signals, and lastly, an R peak is detected. A hybrid (CNN+LSTM) model is implemented to automate arrhythmia categorization on a denoised ECG signal. Comparative analysis demonstrates that the suggested model outperforms contemporary models in terms of various performance factors.
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Shukla et al. (2023) studied Arrhythmia. Hybrid (CNN+LSTM) model with three-step preprocessing vs. Contemporary models and raw input signal was evaluated on Classification accuracy for arrhythmia categorization. A hybrid CNN+LSTM model with three-step preprocessing outperformed contemporary models in automating arrhythmia categorization on denoised ECG signals.
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