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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Zougagh, Lahcen
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Epilepsy detection using wavelet transform, genetic algorithm, and decision tree classifier Zougagh, Lahcen; Bouyghf, Hamid; Nahid, Mohammed; Sabiri, Issa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3447-3455

Abstract

This work presents a unique detection approach for classifying epilepsy using the CHB_MIT dataset. The suggested system utilizes the discrete wavelet transform (DWT) technique, genetic algorithm (GA), and decision tree (DT). This model consists of three distinct steps. In the first one, we present a feature extraction method that uses a DWT of four levels on electroencephalogram (EEG) and electrocardiogram (ECG) signals. The second step is the process of feature selection, which entails the elimination of irrelevant features in order to produce datasets of superior quality. This is achieved via the use of correlation and GA techniques. The reduction in dimensionality of the dataset serves to decrease the complexity of the training process and effectively addresses the problem of overfitting. The third step utilizes a DT algorithm to make predictions based on the data of epileptic patients. The performance evaluation layer encompasses the implementation of our prediction model on the CHB-MIT dataset. The results achieved from this implementation show that using feature selection techniques and an ECG signal as additional information increases the detection model's performance. The averaging accuracy is 98.3%, the sensitivity is 96%, and the specificity is 99%.
A PSO-SVM-Based Approach for Classifying ECG and EEG Bio signals in Seizure Detection Zougagh, Lahcen; Bouyghf, Hamid; Nahid, Mohammed
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1159

Abstract

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.
Co-Authors Bouyghf, Hamid Nahid, Mohammed Sabiri, Issa