IoT-enabled ECG-based heart disease prediction using three-layer deep learning and meta-heuristic approach - Mishra, Jyoti; Tiwari, Mahendra (Signal, image and video processing, 2024)
IoT-enabled electrocardiogram (ECG) devices are used to identify abnormal heart activity in the ECG signals and predict heart disease. Patients wear these devices, which send the ECG data to a healthcare provider for analysis. To investigate the information and forecast the likelihood of heart ailment, deep learning (DL) algorithms are used. DL-based approach for heart disease calculation using ECG signals would involve the following steps: Initially, IoT-based ECG signals from both healthy individuals and individuals with heart disease would be collected. Then, in preprocessing, the ECG signals are preprocessed via finite impulse response. Next, for feature extraction, the features such as P -wave, ST-segment, R -peak locations, heart rate variability, ST-segment, PQ-segment, T -wave, and QRS complex duration, and continuous wavelet transform and improved mutual information are extracted from the preprocessed signals. Then, for feature selection, the optimal structures are selected from the extracted features using the new hybrid optimization model—alpha spider customized dwarf mongoose optimizer, which is the combination of the standard tunicate swarm algorithm and slime mould algorithm. Finally, the heart disease is detected using the new three-layer framework, which encloses the “convolutional neural networks, bidirectional long-short term memory, and recurrent neural networks”. The overall performance of the proposed methodology is assessed by means of the performance metrics such as “accuracy, sensitivity, specificity, precision, recall, FPR, and FNR”. The suggested methodology is implemented in the platform of MATLAB.