Analysis of Psychiatric Disorders from EEG Signals Using Machine-Learning Techniques
Date
2023-05-30
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh
Abstract
Over the past few years, Psychiatric Disorders (PD) have had a significant impact on global
health and their prevalence has been leading towards major adversities like functional disabilities
and even suicide. These disorders can be divided into some major and specific categories which
show different symptoms and have different remedies accordingly. This study makes an attempt
to tackle this problem head on and detect said disorders to allow the patients to take necessary
actions before the point of no return. For efficient and trustworthy detection of PD, a Machine
Learning (ML) approach has been taken and different algorithms were run on the chosen dataset.
The dataset that was used for this study was collected from Neuroimaging (NI),
Electroencephalography (EEG), tests which have a variety of distinctive features. It was observed
that EEG is a reliable and effective way of collecting brain signals which can later be used in
different studies like this one. Judging by the magnitude of the samples taken by the EEG device,
it was decided that ML would be a very useful tool in this regard and with good accuracies, an
acceptable structure can be created. The goal of this study to make a contribution to application of
machine learning algorithms in medical sciences and also to call attention to the capabilities of
EEG in the prompt detection of PD.
From the findings of this study, it can be observed that very high accuracy was obtained for both
the binary and multiclass classifications. The results were tabulated taking samples by using
feature selection and feature extraction methods. The highest accuracy for main disorder for
multiclass classification was 80.69% and that of the specific disorder was 87.52% both of which
used SPARSE PCA feature extraction method. This study makes a solid attempt at addressing this
rising issue with a very satisfactory approach and thus makes a fruitful contribution to the medical
and data science field for addressing similar adversities in the process
Description
Supervised by
Mr. Fahim Faisal,
Assistant Professor,
Department of Electrical and Electronics Engineering (EEE)
Islamic University of Technology (IUT)
Board Bazar, Gazipur-1704, Bangladesh
Keywords
Citation
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