Key result
Custom convolutional neural networks using denoised ECG signals outperformed competitive models in various performance metrics for arrhythmia classification.
Why the study?
The probability of detecting arrhythmia increases if denoised ECG signals are used rather than raw input signals, prompting evaluation of signal filtering methods.
Does the use of denoised ECG signals with custom convolutional neural networks improve arrhythmia classification accuracy compared to raw signals?
Does the use of denoised ECG signals with custom convolutional neural networks improve arrhythmia classification accuracy compared to raw signals?
Applying custom convolutional neural networks to denoised ECG signals improves arrhythmia classification accuracy compared to using raw signals.
Hypothesis-generating for AI arrhythmia detection; requires prospective clinical validation before practice integration.
In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart's rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics.
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Mishra et al. (2022) studied Arrhythmia. Custom convolutional neural networks (CCNNs) with denoised ECG signals vs. Raw input signal and competitive models was evaluated on Classification accuracy. Custom convolutional neural networks using denoised ECG signals outperformed competitive models in various performance metrics for arrhythmia classification.
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