Heart disease detection using deep learning methods from imbalanced ECG samples

Abstract

Heart disease (HD) is a fatal disease which takes the lives of maximum people compared to other diseases across the world. Early and accurate detection of the disease will help to save many valuable lives. The HD can be detected from medical tests, Electrocardiogram (ECG) signal, heart sounds, Computed Tomography (CT) Images etc. Out of all types of detection of HD from ECG signals plays a vital role. In this paper, the ECG samples of the subjects have been considered as the required inputs to the HD detection model. In recent past, many useful articles have been reported for classification of HD using different machine learning (ML) and deep learning (DL) models. It is observed that with imbalanced HD data the detection accuracy is lower. With an objective to achieve better detection of HD, suitable DL and ML models have been identified in this paper and the required classification models have been developed and tested. The Generative Adversarial Network (GAN) model is chosen with an objective to deal with imbalanced data by generating and using additional fake data for detection purpose. Further, an ensemble model using long short-term memory (LSTM) and GAN is developed in this paper which demonstrates higher performance compared to individual DL model used in this paper. The simulation results using standard MIT-BIH show that the proposed GAN-LSTM model provides the highest accuracy, F1-score and area under curve (AUC) of 0.992, 0.987 and 0.984 respectively compared to other models. Similarly, for PTB-ECG dataset the GAN-LSTM model outperforms all other models with accuracy, F1-score and AUC of 0.994, 0.993 and 0.995 respectively. It is observed that out of the five models investigated, the GAN model performs the best whereas the detection potentiality of the NB model is the lowest. Further research work can be carried out by choosing all other different ensemble models and using other different datasets and the performance can be similarly obtained and compared. The proposed best detection methodology can also be applied to other diseases and healthcare problems.