Automatic heart disease class detection using convolutional neural network architecture-based various optimizers-networks

Early heart disease class detection is of great interest to reduce the mortality rate. In this context, computational techniques have been proposed to solve this issue. Thus, here, a deep learning architecture is proposed to automatically classify the patient’s Electrocardiogram (ECG) signal into a specific class according to the ANSI–AAMI standards. The proposed methodology is a multi-stage technique. The first stage combines an R–R peak extraction with a low-pass filter applied on the ECG data for noise removal. The second stage shows the proposed convolutional neural network-based fully connected layers architecture, using different network optimizers. Different ECG databases, including challenging tasks, have been used for validation purpose. The whole system is implemented on both CPU and GPU for complexity analysis. For the predicted improved PTB data set, the classification accuracy results achieve 99.37%, 99.15% and 99.31% for training, validation and testing, respectively. Besides, for the MIT-BIH-database, the training, validation and testing accuracies are 99.5%, 99.06% and 99.34%, respectively. A top F1-score of 0.99 is obtained. Experimental results show a high achievement compared to the state-of-the-art models where the implementation of GPU confirms the low computational complexity of the system.