The Effect of Emotions and Parkinson’s Disease Prediction Using Classification Learner Models with EEG Dataset Generation

S. Arockiya Selvi () and T. Kamalakannan
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S. Arockiya Selvi: VISTAS, Research Scholar, Department of Computer Science
T. Kamalakannan: VISTAS, Research Supervisor, Department of Computer Science

A chapter in Proceedings of the International Conference on Digital Transformation in Business: Navigating the New Frontiers Beyond Boundaries (DTBNNF 2024), 2024, pp 566-577 from Springer

Abstract: Abstract Nearly all hazardous illnesses that affect humans are mostly brought on by emotion. Emotion influences blood pressure, heart rate, renal functionality as well as a few neurological issues. Parkinson’s disease movement dysfunction is one of the neurological issues. This problem is caused by a decrease in dopamine secretion in our brains. Dopamine production abnormalities are associated with essential hypertension. Dopamine is important in anxiety modulation in various parts of the brain. Patients with PD frequently encounter that moments of acute stress make their motor symptoms, such as gait freezing, dyskinesia, and tremors, worse. We can say with absolute certainty that emotions play a crucial role in Parkinson’s disease. The Classification Learner algorithms and the EEG dataset, which covers the clinical range of Parkinson’s disease progression, were the main topics of this research study. We look at the supervised machine learning algorithms: Course Gaussian SVM, Medium Gaussian SVM, Quadratic SVM, and Linear SVM. This yields the accuracy required to detect Parkinson’s disease early on with the aid of SVM algorithms and the EEG dataset. With MATLAB, we were able to predict the following: accuracy, sensitivity, specificity, precision, and error rate.

Keywords: Parkinson’s disease (PD). Electroencephalogram (EEG). MATLAB; Support Vector Machine (SVM) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-433-4_43

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