Abstract:

In recent days, machine learning (ML) becomes a familiar topic and is extensively used for decision making in various real time applications, particularly healthcare. ML approaches in healthcare make use of massive quantity of healthcare data to enhance the medical services to the patients. At the same time, Epilepsy is unavoidably identified as a critical and persistent neurological illness affecting the human brain. Electroencephalogram (EEG) is commonly employed as an important tool to identify distinct neurological illnesses of the human brain, specifically seizures. The ML approaches find useful to examine the EEG signals to determine the presence of seizures. With this motivation, this paper presents an optimal least square support vector machine (OLS-SVM) based automated epileptic seizure detection tool using EEG signal. The proposed OLS-SVM model incorporates different processes such as pre-processing, classification, and parameter tuning. The EEG signals are initially pre-processed to remove the unwanted signals. In addition, the LS-SVM model is employed to classify the EEG signals into the presence of seizures or not. Moreover, the OLS-SVM model is designed by the parameter optimization of the LS-SVM method utilizing salp swarm optimization algorithm (SSA). The use of SSA as parameter optimization tool for LS-SVM model shows the novelty of the work. For examining the enhanced diagnostic performance of the OLS-SVM technique, a wide range of experiments are performed and the outcomes were investigated in distinct measures.

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