Optical electrocardiogram based heart disease prediction using hybrid deep learning

The diagnosis and categorization of cardiac disease using the low-cost tool electrocardiogram (ECG) becomes an intriguing study topic when contemplating intelligent healthcare applications. An ECG-based cardiac disease prediction system must be automated, accurate, and lightweight. The deep learning methods recently achieved automation and accuracy across multiple domains. However, applying deep learning for automatic ECG-based heart disease classification is a challenging research problem. Because using solely deep learning approaches failed to detect all of the important beats from the input ECG signal, a hybrid strategy is necessary to improve detection efficiency. The main objective of the proposed model is to enhance the ECG-based heart disease classification efficiency using a hybrid feature engineering approach. The proposed model consists of pre-processing, hybrid feature engineering, and classification. Pre-processing an ECG aims to eliminate powerline and baseline interference without disrupting the heartbeat. To efficiently classify data, we design a hybrid approach using a conventional ECG beats extraction algorithm and Convolutional Neural Network (CNN)-based features. For heart disease prediction, the hybrid feature vector is fed successively into the deep learning classifier Long Term Short Memory (LSTM). The results of the simulations show that the proposed model reduces both the number of diagnostic errors and the amount of time spent on each one when compared to the existing methods.