Evaluating Domain Knowledge and Time Series Features for Automated Detection of Schizophrenia from EEG Signals

Over the recent years, Schizophrenia has become a serious mental disorder that is affecting almost 21 million people globally. There are different symptoms to recognize schizophrenia from healthy people. It can affect the thinking pattern of the brain. Delusions, hallucinations, and disorganized spe...

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Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 12; no. 11
Main Authors: Hussain, Saqib, Pirzada, Nasrullah, Saba, Erum, Panhwar, Muhammad Aamir, Ahmed, Tanveer
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2021
Subjects:
Algorithms
Brain
Electroencephalography
Feature extraction
Hallucinations
Machine learning
Mental disorders
Regression models
Schizophrenia
Statistical analysis
Time series
ISSN:2158-107X, 2156-5570
Online Access:Get full text
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Summary:Over the recent years, Schizophrenia has become a serious mental disorder that is affecting almost 21 million people globally. There are different symptoms to recognize schizophrenia from healthy people. It can affect the thinking pattern of the brain. Delusions, hallucinations, and disorganized speech are the common symptoms of Schizophrenia. In this study, we have used electroencephalography (EEG) signals to analyze and diagnose Schizophrenia using machine learning algorithms and found that temporal features performed well as compared to statistical features. EEG signals are the best way to analyze this disorder as they are intimately linked with human thinking patterns and provide information about brain activities. The present work proposes a Machine Learning (ML) model based on Logistic Regression (LR) along with two feature extraction libraries Time Series Feature Extraction Library (TSFEL) and MNE Python toolkit to diagnose Schizophrenia from EEG signals. The results are analyzed based on 5 different sampling techniques. The dataset was cross-validated using leave one subject out cross-validation (LOSOCV) using Scikit learn and achieve greater accuracy, sensitivity, specificity, macro average recall, and macro f1 score on temporal features respectively.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0121160