A PSO-SVM-Based Approach for Classifying ECG and EEG Bio signals in Seizure Detection

Early identification of epileptic activities is essential for clinical analysis and preventing advancement of the disease. Despite the development of neurological diagnostic techniques, the current analysis of epileptic seizures is still relying on a visual interpretation of electroencephalogram (EEG) signal. Neurology specialists manually perform this examination to detect patterns, a process that is both challenging and time-consuming. Biomedical signals, such as EEG and electrocardiogram (ECG), are important tools for studying human brain disorders, particularly epilepsy. This paper aims to develop a system that automatically detects epileptic seizures using discrete wavelet decomposition (DWT), particle swarm optimization (PSO), and support vector machine (SVM), thereby relieving clinicians of their challenging tasks. The proposed system employs the DWT method, PSO, and SVM. This approach has three steps. First, we introduce a method that uses a four-level discrete wavelet transform (DWT) to extract important information from electroencephalogram and electrocardiogram signals by breaking them down into useful features. Second, we optimize the SVM classifier parameters using the PSO algorithm. Finally, we classify the extracted parameters using the optimized SVM. The system achieves an average accuracy of 97.92%, a 100% recall, a 96.15% specificity, and a 0.96 AUC value. Our findings demonstrate the success of this method, showing that the PSO-optimized SVM performs significantly better in classification. In addition, our findings also demonstrate the importance of using ECG signals as supplemental data. One implication of our work is the potential for creating wearable, real-time, customized seizure warning systems. In the future, these systems will be deployed on embedded platforms in real time and validated using larger datasets.