Authors:
Ali Aldujaili
1
;
2
;
Rosa-Zurera Manuel
2
and
Ahmed Meri
3
Affiliations:
1
Department Affairs of Student Accommodation, University of Baghdad, Baghdad, Iraq
;
2
Department of Signal Theory and Communication, University of Alcalá, Alcalá de Henares, Madrid, Spain
;
3
Department of Medical Instrumentation Techniques Engineering, Al-Hussain University College, Karbala, Iraq
Keyword(s):
EEG, Parkinson’s Disease, Early Detection, Deep Learning, YAMNet.
Abstract:
Parkinson’s disease is a neurodegenerative disorder with a progressively debilitating impact on patients’ movement in terms of cognitive and motor aspects. Early detection is crucial for effective disease management and better patient outcomes. There are many techniques to detect this disease, but one of the most interesting methods to achieve early detection of Parkinson’s disease is electroencephalography, which is a non-invasive and cost-effective diagnostic tool to measure brain activity. Recent studies have shown that deep learning networks can handle complex data to analyse it and extract features. One of these neural networks is called Yet Another Mobile Network (YAMNet), which was originally proposed to analyse speech signals using time-frequency information. In this research, a novel approach using YAMNet is presented for the detection of Parkinson’s disease patients using electroencephalogram brain signals, as the frequency information seems very relevant for Parkinson’s di
sease detection. The proposed approach was evaluated with an open access dataset available on the Internet, composed of electroencephalogram recordings from Parkinson’s disease patients and healthy control people, obtaining an accuracy rate of 98.9%. The results suggest that YAMNet could be an encouraging tool for the initial, non-invasive detection of Parkinson’s disease. This may improve patient treatments and stimulate future research in the field.
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