A Novel Improved Method for Prediction of Heart Disease using ECG Hybrid 0F PTB-ECG and MIT-BIH Signal Dataset

Abstract

Heart disease is the leading cause of death worldwide, making early detection critical. Various diagnostic methods, including clinical tests, CT, MRI, ECG, and impedance cardiography, are commonly used to detect heart disease. However, traditional coronary artery disease (CAD) detection methods using ECG data face challenges due to the time-series nature of ECG signals, which complicates handling multiple classes. To address this, the study proposes a deep learning-based approach that enhances CAD detection accuracy by integrating two models Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with a hybrid dataset combining PTB-ECG and MIT-BIH data. This hybrid dataset consists of two target classes: normal (0) and abnormal (1), created by merging all MIT-BIH classes with the PTB-ECG normal class as “0” and abnormal samples from PTB-ECG as “1”. Pre-processing was performed using Gaussian distribution for normalization, standardization, and outlier removal. The study applied four classification approaches: CNN, CNN+LSTM, CNN with SMOTE-balanced data, and CNN+LSTM with SMOTE-balanced data. Results indicate that CNN with SMOTE-balanced data achieved the best performance, with training metrics of 0.9998 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score for both classes. Testing results using CNN+SMOTE reached 0.9991 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. The model surpasses state-of-the-art studies, which achieved 0.992 accuracy and F1-score of 0.986 on PTB-ECG and MIT-BIH datasets, respectively. This study demonstrates that combining CNN with SMOTE on a hybrid dataset can significantly improve CAD detection accuracy.