Key result
Deep learning approaches for ECG arrhythmia classification show promise, though there is still room to improve performance by reducing preprocessing and computational expense.
Why the study?
Automated computer aids are needed to support cardiologists in rapidly classifying ECG arrhythmias, because manual identification of heartbeat classes by doctors can take a long time.
This review highlights the potential of various deep learning models for automated ECG arrhythmia classification while identifying computational expense and preprocessing as key areas for future optimization.
Deep learning ECG tools merit further development; leaves open efficiency gains before clinical viability.
An automated computer aid remains relevant to support cardiology specialists in diagnosing heart disorders and rapidly classifying arrhythmias by using an electrocardiogram (ECG), which is among the most regularly utilized techniques to identify health disorders because hand identification of these heart-beat classes by doctors might take a long time. In this paper, we investigated and reviewed diverse research that worked on ECG arrhythmia identification by employing deep learning approaches. We illustrated and discussed the performance and approaches adopted to identify ECG heart arrhythmias by six commonly utilized methods, including MLP (Multilayer Perceptron), CNN (Convolutional Neural Network), DBN (Deep Belief Network), RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). We considered various limits and disclosed that there is yet space to extend the classification’s performance, precisely by reducing the preprocessing and principally the computational expense. Such a managed paper survey provides specialists with a nearly clear view of some elements of ECG classification methods and allows them to explore points unmet until now.
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Sraitih et al. (2022) conducted a review in ECG heartbeat arrhythmias. Deep learning approaches was evaluated on ECG arrhythmia identification performance. Deep learning approaches for ECG arrhythmia classification show promise, though there is still room to improve performance by reducing preprocessing and computational expense.
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