Manifold grasshopper optimization based extremely disruptive vision transformer model for automatic heart disease detection in raw ECG signals

Automated detection of cardiovascular diseases based on heartbeats is a difficult and demanding task in signal processing because the routine analysis of the patient’s cardiac arrhythmia is crucial to reducing the mortality rate. Detecting and preventing these deaths requires long-term monitoring and manual examination of electrocardiogram (ECG) signals, which takes a lot of time. This article uses an optimized Vision Transformer technique to effectively detect heart disease. The four key processes are pre-processing input data, feature extraction from pre-processed data, and optimal feature selection and classification to detect heart disease. In the pre-processing phase, single-channel adaptive blind source separation is used for artifact removal and empirical mode decomposition for noise reduction of the ECG signal. After pre-processing, the ECG signal is fed into the Enhanced Pan-Tompkins algorithm (EPTA) and the Hybrid Gabor-Walsh-Hadamard transform (HGWHT) for feature extraction. The extracted feature is selected using a Manifold Grasshopper Optimization algorithm (MGOA). Finally, an Optimized Vision Transformer (OVT) detects heart disease. The experiment is carried out on PTB diagnostic ECG and PTB-XL database, a publicly accessible research datasets. The experiment obtained the following values: accuracy 99.9%, sensitivity 98%, F1 score 99.9%, specificity 90%, processing time 13.254 s, AUC 99.9% and MCC 91% using PTB diagnostic ECG. On the other hand, the proposed method has obtained an accuracy of 99.57%, f1-score of 99.17% and AUC of 99% using PTB-XL dataset. Thus, the overall findings prove that the proposed method outperforms the existing methodology.