Detection of Schizophrenia from EEG Signal–A Convolution Neural Network Framework using Small Dataset
Authors
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Narasinha Dutt College (Calcutta University) ,IN
Angshuman Sarkar -
Narasinha Dutt College (Calcutta University) ,INShambo Saurav Mallik
DOI:
https://doi.org/10.24906/isc/2023/v37/i5/44895Keywords:
Schizophrenia, Deep Learning, 1D-CNN, CNN-LSTM, Sensitivity, Specificity.Abstract
Schizophrenia (SZ) is a brain disorder that disrupts normal thoughts, actions and emotions of a person. A common, cost-effective method of diagnosing SZ is using electroencephalography (EEG) signals. Since EEG signals are collected from many different channels over a long period of time, it sometimes become difficult for physicians to interpret numerous patterns of signals. In this work, authors proposed a number of deep learning (DL) based methods to separate the SZ patients from normal subjects from their EEG data using a small dataset of 14 SZ patients and 14 healthy subjects. Results were compared with works of others and the proposed CNN-LSTM method was found to perform better.
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