Diagnosis of Delusion and Hallucination from Schizophrenia Patient Using RADWT

Area of Science:

• Neuroscience
• Medical Imaging
• Signal Processing

Background:

• Schizophrenia diagnosis is challenging, often occurring years after illness onset.
• Current diagnostic methods, including clinical screening and brain imaging, have limitations in early detection and subtyping.
• Distinguishing between schizophrenia subtypes like hallucination and delusion remains a significant clinical hurdle.

Purpose of the Study:

• To propose an early detection system for schizophrenia subtypes using electroencephalography (EEG) signals.
• To enhance the accuracy of schizophrenia subtyping through advanced signal processing techniques.
• To prevent disease progression by enabling earlier diagnosis and intervention.

Main Methods:

• Acquired EEG signals from 25 patients during cognitive tasks (number counting, DSM screening) and eye rest.
• Utilized the high Q-factor of the Redundant Discrete Wavelet Transform (RADWT) for detailed EEG signal analysis.
• Optimized the dilation factor in RADWT to influence signal resolution, Q-factor, redundancy, and ringing for precise energy distribution.

Main Results:

• The proposed RADWT method demonstrated distinct sub-band energy patterns in EEG signals during cognitive tasks.
• Successfully distinguished between hallucination and delusion subtypes in 21 out of 25 patients.
• Achieved an 84% accuracy rate in the sub-classification of schizophrenia types.

Conclusions:

• Early detection of schizophrenia subtypes is feasible through EEG signal analysis with RADWT.
• The optimized RADWT approach offers a promising tool for improving diagnostic accuracy in clinical settings.
• This method has the potential to significantly impact patient outcomes by enabling timely and specific treatment.