Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.

Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in misdiagnosis and suboptimal treatment outcomes. This study aimed to identify EEG-based biomarkers that can differentiate SZ from BD using multiscale fuzzy entropy (MFE) and relative power (RP) analyses and to evaluate their diagnostic utility using machine learning. EEG data were collected from 65 patients with SZ and 49 patients with BD under resting-state conditions. The MFE and RP were calculated for the bilateral frontal, central, and parietal regions using 30 s EEG segments. For MFE, the band-scale fuzzy entropy (FuzzyEn) was determined across the theta, alpha, beta, and gamma bands based on simulation results demonstrating an inverse relationship between scale factors and frequency components. RP was derived by segmenting the EEG data into 2 s intervals with a 500 ms moving window. A support vector machine (SVM) was used to differentiate between patients with SZ and BD based on band-scale FuzzyEn and RP. The SVM classifier achieved an accuracy of 78.94%, a sensitivity of 81.53%, and a specificity of 75.51%. Patients with SZ showed higher theta-scale FuzzyEn in the right frontal, left central, and bilateral parietal regions; higher alpha-scale FuzzyEn in the right parietal region; and increased theta power in the bilateral frontal, central, and right parietal regions. These differences remained robust after controlling for medication effects. These findings demonstrate the potential of combining MFE, RP, and machine learning to differentiate between SZ and BD, contributing to improved diagnostic precision in psychiatric disorders.