Why the study?
Convenient discovery of anomalies in ECG signals plays an important role in monitoring patient condition, motivating the use of machine learning techniques such as CNN and RNN for ECG signal classification.
Machine learning techniques, particularly CNNs and RNNs, are effective for feature extraction and accurate classification of ECG signals.
CNN-based ECG classification may support monitoring; leaves open rigorous prospective validation before clinical adoption.
In recent days Machine Learning has become major study aspect in various applications that includes medical care where convenient discovery of anomalies in ECG signals plays an important role in monitoring patient's condition regularly. This study concentrates on various MachineLearning techniques applied for classification of ECG signals which include CNN and RNN. In the past few years, it is being observed that CNN is playing a dominant role in feature extraction from which we can infer that machine learning techniques have been showing accuracy and progress in classification of ECG signals. Therefore, this paper includes Convolutional Neural Network and Recurrent Neural Network which is being classified into two types for better results from considerably increased depth.
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Naidu et al. (2020) studied this question.
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