Abstract
Parkinson’s disease (PD) is a severe incurable neurological disorder. It is mostly characterized by non-motor symptoms like fatigue, dementia, anxiety, speech and communication problems, depression, and so on. Electroencephalography (EEG) play a key role in the detection of the true emotional state of a person. Various studies have been proposed for the detection of emotional impairment in PD using filtering, Fourier transforms, wavelet transforms, and non-linear methods. However, these methods require a selection of basis and are confined in terms of accuracy. In this paper, tunable Q wavelet transform (TQWT) is proposed for the classification of emotions in PD and normal controls (NC). EEG signals of six emotional states namely happiness, sadness, fear, anger, surprise, and disgust are studied. Power, entropy, and statistical moments based features are elicited from the highpass and lowpass sub-bands of TQWT. Six features selected by statistical analysis are classified with a k-nearest neighbor, probabilistic neural network, random forest, decision tree, and extreme learning machine. Three performance measures are obtained, maximum mean accuracy, sensitivity, and specificity of 96.16%, 97.59%, and 88.51% for NC and 93.88%, 96.33%, and 81.67% for PD are achieved with a probabilistic neural network. The proposed method proved to be very effective such that it classifies emotions in PD and could be used as a potential tool for diagnosing emotional impairment in hospitals.
Highlights
Parkinson’s Disease (PD) is a severe non-curable neurological disorder
Eleven features based on power, energy, entropy, and statistical moments are extracted from the subbands
Probabilistic neural network proved to be effective for the lower Q value while for higher quality factor random forest classifier outperforms other
Summary
Parkinson’s Disease (PD) is a severe non-curable neurological disorder. The symptoms mainly include deficits of motor movement, fatigue, depression, anxiety, dementia, speech communication problems, pain, cognitive problems, etc. The multiple features extracted from EEG signals have been classified by the decision tree classification method in [17]. In [23], higher-order spectral features elicited from the rhythms of filtered EEG signals have been classified with KNN and SVM. Recurrent quantification analysis has been used to extract the features from the rhythms of EEG signals These features have been classified with extreme learning machine (ELM) [25]. In [40], emotions have been recognized using optimized variational mode decomposition and ELM based feature extraction and classification method. The methods used in this literature involves an analysis of EEG signals using statistical tests, direct feature extraction from the signals, filtering techniques, rhythmic analysis, FFT, S-transform, wavelet transform, empirical wavelet transform, empirical mode decomposition and singular value decomposition. A rigorous analysis of emotions is done with the aid of several machine learning methods
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