Key result
An automated framework using GModPCA and SVM achieved 100% accuracy for the classification between normal and epileptic EEG signals, with lower time and space complexities compared to PCA.
The proposed GModPCA and SVM framework provides highly accurate and computationally efficient automated epileptic seizure detection in EEG signals.
May support automated EEG frameworks for efficient seizure detection; leaves open prospective clinical validation before adoption.
BACKGROUND AND OBJECTIVE: Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. METHODS: Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. RESULTS: The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space complexities of GModPCA are less as compared to PCA. CONCLUSIONS: This study suggests that GModPCA and SVM could be used for automated epileptic seizure detection in EEG signal.
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Jaiswal et al. (2017) studied Epilepsy. Global modular PCA (GModPCA) and Support Vector Machine (SVM) vs. PCA and existing methods was evaluated on Classification accuracy between normal and epileptic signals. An automated framework using GModPCA and SVM achieved 100% accuracy for the classification between normal and epileptic EEG signals, with lower time and space complexities compared to PCA.
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