Parkinsons Disease Detection via Resting-State Electroencephalography
Using Signal Processing and Machine Learning Techniques
- URL: http://arxiv.org/abs/2304.01214v1
- Date: Wed, 29 Mar 2023 06:03:05 GMT
- Title: Parkinsons Disease Detection via Resting-State Electroencephalography
Using Signal Processing and Machine Learning Techniques
- Authors: Krish Desai
- Abstract summary: Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons.
EEG indicates abnormalities in PD patients.
One major challenge is the lack of a consistent, accurate, and systemic biomarker for PD in order to closely monitor the disease with therapeutic treatments and medication.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor
deficits due to advancing degeneration of dopaminergic neurons. PD patients
report experiencing tremor, rigidity, visual impairment, bradykinesia, and
several cognitive deficits. Although Electroencephalography (EEG) indicates
abnormalities in PD patients, one major challenge is the lack of a consistent,
accurate, and systemic biomarker for PD in order to closely monitor the disease
with therapeutic treatments and medication. In this study, we collected
Electroencephalographic data from 15 PD patients and 16 Healthy Controls (HC).
We first preprocessed every EEG signal using several techniques and extracted
relevant features using many feature extraction algorithms. Afterwards, we
applied several machine learning algorithms to classify PD versus HC. We found
the most significant metrics to be achieved by the Random Forest ensemble
learning approach, with an accuracy, precision, recall, F1 score, and AUC of
97.5%, 100%, 95%, 0.967, and 0.975, respectively. The results of this study
show promise for exposing PD abnormalities using EEG during clinical diagnosis,
and automating this process using signal processing techniques and ML
algorithms to evaluate the difference between healthy individuals and PD
patients.
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