A survey of deep learning approaches for classifying ECG heartbeat arrhythmias

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.