Salp Swarm Optimization Based Machine Learning Algorithm on Epileptic Seizure Detection and Classification Model

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Suresh Srirangam


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

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