Parkinson’s disease detection using EEG signals analysis based on Walsh Hadamard transform
Subject Areas : Intelligent Multimedia Processing and Communication Systems (IMPCS)
Yasamin
Ezazi
1
(MSc student/Faculty of Biomedical Engineering/Sahand University of Technology)
Peyvand
Ghaderyan
2
(biomedical engineering faculty, Sahand University of Technology, Tabriz, Iran)
Keywords: K nearest neighbor, Walsh Hadamard transform, Parkinson’s Disease, Reinforcement-learning task, Support vector machine,
Abstract :
Background: Parkinson's disease (PD) is one of the most important diseases of the nervous system that occurs due to the degeneration of dopaminergic neurons in the substantia nigra. Because of increasing prevalence rate, lack of specific treatment, and aggravation symptoms over time, PD detection is very important for the optimal control of patients' life. Therefore, the development of non-invasive, low-cost and reliable clinical diagnostic methods play an essential role to help doctors in diagnosis, slowing progressions of the disease and providing better control strategies to improve the quality of patients' life. Among diagnostic methods, recording and analyzing the electroencephalogram (EEG) signal as a low-cost and non-invasive approach has attracted a lot of attention.Method: EEG signal analysis in the time domain contains important information, but does not include the frequency information. Hence, this study is based on extracting new frequency features from the EEG signal using Walsh-Hadamard transform (WHT). WHT converts the signal from the time domain into the frequency domain and decompose it into orthogonal and rectangular waves. In this method, after calculating the Walsh coefficients, a set of features such as entropy, impulsive metrics, basic and high-order statistical features have been extracted from these coefficients. Subsequently, the discriminating capability of the presented method has been assessed using two classifiers namely support vector machine and k-nearest neighbor to classify PD patients from the healthy group.Results: The proposed method has been evaluated using the EEG signals of 28 healthy individuals and 28 patients with PD in two medication states (ON and OFF) during the reinforcement learning task. The obtained results have shown that this method is able to detect PD by using the entropy feature, support vector machine, and k nearest neighbor with acceptable accuracy of 99.95% and 99.98%, respectively. The good performance of entropy feature in comparison of other ones can be attributed to non-linear and non-stationary nature of EEG signal.Conclusion: In this study, a non-invasive, low-cost, and reliable method for PD detection using EEG signal analysis has been proposed. This algorithm is a multi-stage technique with a feature extraction approach based on WHT, entropy feature, and support vector machine and k-nearest neighbor classifiers. The reported results indicate that this method is effective in PD detection while being simple and easy, as well as being robust to the clinical factor of medication status.
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