Open AccessFrequency domain analysis for the classification of parkinson’s disease patients
Author(s) -
S. Kamalraj,
K.N. Rejith,
G. K. D. Prasanna Venkatesan
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/561/1/012126
Subject(s) - pattern recognition (psychology) , artificial intelligence , support vector machine , disgust , entropy (arrow of time) , speech recognition , linear discriminant analysis , approximate entropy , computer science , feature extraction , frequency domain , mathematics , anger , psychology , physics , quantum mechanics , psychiatry , computer vision
In this paper, Emotional recognition in Parkinson’s disease (PD) has been analyzed in frequency domain using Entropy, Energy-Entropy and Teager Energy-Entropy features. Classification results were observed using three classifiers namely Probabilistic Neural Network, K-Nearest Neighbors Algorithm and Support Vector Machine. Emotional EEG stimuli such as happiness, sadness, fear, anger, surprise, and disgust were used to categorize the PD patients and healthy controls (HC). For each EEG signal, the alpha, beta and gamma band frequency features are obtained for three different feature extraction methods (Spectral Entropy, Spectral Energy-Entropy, and Spectral Teager Energy-Entropy). The proposed Spectral Energy–Entropy feature performs well for all six emotions for different classifiers when compared to other features, whereas different features with classifiers give variant results for few emotions with highest accuracy of 96.8%.