Detecting Schizophrenia via EEG Data Analysis: A Review of Recent Advances

Schizophrenia is a complex mental disorder that affects approximately 1% of the global population, with equal prevalence in men and women. Accurate diagnosis relies on a combination of psychiatric evaluation, medical record review, and brain imaging to rule out other conditions. However, these methods are subjective and prone to inconsistencies.
To address this challenge, researchers have turned to electroencephalography (EEG) data analysis. EEG measures the electrical activity of the brain and can provide valuable information for diagnosing schizophrenia. By analyzing EEG data from patients with schizophrenia and healthy controls, researchers have identified distinct patterns of brain activity that can be used to distinguish between the two groups.
These patterns are derived from the positive symptoms (hallucinations, delusions), negative symptoms (social withdrawal, lack of motivation), and cognitive symptoms (problems with attention, memory, decision-making) of schizophrenia. The analysis is based on the assumption that the rest state may provide information complementary to that obtained during the arithmetic task phase.
The study used a novel machine learning approach called fuzzy entropy algorithm to analyze EEG data from patients and healthy controls. The algorithm divides the data into epochs, and a one-dimensional fuzzy entropy algorithm is applied to each epoch. The mean of fuzzy entropy values from all epochs in a cortical region becomes the fuzzy entropy feature for that region. Cortical regions considered include frontal, central, parietal, temporal, and occipital. Fuzzy entropy features are also computed from EEG data during the first and second resting phases.
The results of the study showed that the proposed approach can accurately distinguish between schizophrenia patients and healthy controls with a high degree of accuracy. The findings suggest that EEG data analysis may provide a valuable tool for diagnosing schizophrenia, particularly in cases where other diagnostic methods are inconclusive or difficult to interpret.
In conclusion, this study demonstrates the potential of EEG data analysis for demystifying complex concepts and providing a more accurate method for diagnosing schizophrenia. By leveraging cutting-edge machine learning techniques and brain imaging technologies, researchers can uncover novel insights into the neural mechanisms underlying this devastating mental disorder, paving the way for improved diagnostic accuracy and patient outcomes.