Smooth Support Vector Machine Based on Polynomial Function for Depression Detection Using Electroenchephalogram (EEG) Signal
Annisatul Nikmah (a), Santi Wulan Purnami (a*), Shofi Andari (a), Margarita M. Maramis (b), Wardah R. Islamiyah (c), Jasni Muhammad Zain (d)

a) Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
*santi_wp[at]statistika.its.ac.id
b) Department of Psychiatry, Faculty of Medicine, Airlangga University, Surabaya 60131, Indonesia
c) Department of Neurology, Faculty of Medicine, Airlangga University, Surabaya 60131, Indonesia
d) Institute for Big Data Analytics and Artificial Intelligence(IBDAAI), Universiti Teknologi Mara (UiTM), Shah Alam 40450, Malaysia

Abstract

Mental health is an important issue today as mental illness as a global health problem ranks fifth in the world. Depression is a major illness that affects many people around the world and people suffering from depression often have a low level of awareness. It is still common to detect depression using clinical questionnaires. However, using questionnaires for large-scale surveys will consume large human and material resources. Therefore, scientists and researchers from around the world are working to find alternative and objective ways to detect mental depression, especially through EEG signal data. Several studies have shown that abnormal patterns in alpha waves in EEG signals are associated with depression, but beta, delta, theta, and gamma waves can also be used for depression detection. EEG signal preprocessing is required before classification by filtering using Finite Impulse Response (FIR). Furthermore, EEG signal data will be classified using one of the Machine Learning methods, namely Support Vector Machine (SVM) because from some existing research SVM provides superior performance compared to other methods. This research proposes Piecewise Polynomial Smooth Support Vector Machine (PPWSSVM) and Spline Smooth Support Vector Machine (Spline SSVM) for the classification method and data will be taken from patients at the Psychiatric Poli Dr. Soetomo Hospital. The results of this study are expected to be able to provide assistance and alternative methods related to the objective diagnosis of depression.

Keywords: Depression-EEG-PPWSSVM-Spline SSVM

Topic: Mathematics and Statistics