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Tunable Q wavelet transform based emotion classification in Parkinson's disease using Electroencephalography.

Murugappan Murugappan1, Waleed Alshuaib2, Ali K Bourisly2

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Summary
This summary is machine-generated.

This study introduces a novel tunable Q wavelet transform (TQWT) method for classifying emotions in Parkinson's disease (PD) patients using electroencephalography (EEG) signals. The TQWT approach shows high accuracy in detecting emotional states, offering a potential diagnostic tool for PD.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Parkinson's disease (PD) is a neurological disorder with significant non-motor symptoms, including emotional impairments.
  • Electroencephalography (EEG) is a valuable tool for assessing emotional states.
  • Existing methods for emotion detection in PD using EEG have limitations in accuracy and basis selection.

Purpose of the Study:

  • To propose and evaluate the tunable Q wavelet transform (TQWT) for classifying emotions in individuals with Parkinson's disease (PD) and normal controls (NC).
  • To assess the effectiveness of TQWT in analyzing EEG signals for six distinct emotional states.

Main Methods:

  • EEG signals corresponding to happiness, sadness, fear, anger, surprise, and disgust were analyzed.
  • Features including power, entropy, and statistical moments were extracted from TQWT sub-bands.
  • Selected features were classified using k-nearest neighbor, probabilistic neural network, random forest, decision tree, and extreme learning machine algorithms.

Main Results:

  • The tunable Q wavelet transform (TQWT) demonstrated high performance in classifying emotions from EEG signals.
  • A probabilistic neural network achieved maximum mean accuracy of 96.16% for NC and 93.88% for PD.
  • High sensitivity (97.59% for NC, 96.33% for PD) and specificity (88.51% for NC, 81.67% for PD) were recorded.

Conclusions:

  • The proposed TQWT method is highly effective for emotion classification in Parkinson's disease (PD) patients.
  • This technique shows potential as a diagnostic tool for identifying emotional impairment in clinical settings.
  • TQWT offers an improved approach compared to traditional methods for EEG-based emotion analysis in PD.