Abstract
Classification of Emotional States in Parkinson’s Disease Patients using Machine Learning Algorithms
Highlights
Electroencephalogram (EEG) signals or brain signals are used widely to diagnose epilepsy, sleep disorders, depth of anesthesia, coma, encephalopathy, and brain death and for detecting tumors, stroke and other focal brain disorders as front line method
Emotional processing with disorder were analyzed using neuroimaging techniques such as functional magnetic resonance imaging and positron emission tomography (PET) and these techniques helps to identify the specific region of emotional functions[1].In previous research works, emotional processing in patients with disorder were analyzed using neuroimaging techniques such as functional magnetic resonance imaging and positron emission tomography (PET) and these techniques helps to identify the specific region of emotional functions[1]
The time domain features EN & Energy- Entropy (EEN) and the frequency domain features Spectral Entropy (SEN) &SEEN features were extracted from the Parkinson’s disease (PD) and healthy controls (HC) EEG signals and the results were analyzed
Summary
Electroencephalogram (EEG) signals or brain signals are used widely to diagnose epilepsy, sleep disorders, depth of anesthesia, coma, encephalopathy, and brain death and for detecting tumors, stroke and other focal brain disorders as front line method. For Spectral Entropy (SEN) feature extraction process, from the filtered values, x(q) were first Fourier transformed to using the Equation(3), N Table 2c: Results of Spectral Energy-Entropy feature using PNN classifier factor (s)
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