Why the study?
Accurate and timely diagnosis of bipolar disorder remains challenging, and EEG entropy measures offer a complementary nonlinear approach that is less explored than power spectral density changes.
Do EEG entropy measures combined with machine learning accurately distinguish individuals with bipolar disorder from healthy controls?
Do EEG entropy measures combined with machine learning accurately distinguish individuals with bipolar disorder from healthy controls?
Multiscale dispersion entropy from resting-state EEG, particularly in the frontal brain region, shows high accuracy (95%) in distinguishing bipolar disorder patients from healthy controls.
Answers are grounded in this paper and cite their sources.
May support EEG-based bipolar disorder classification tools; hypothesis-generating from Level 4 case-control data and needs prospective validation.
Bipolar disorder (BD) is a common psychiatric condition with varying severity, characterized by alternating episodes of mania or hypomania and depression, making accurate and timely diagnosis challenging. Electroencephalography (EEG) nonlinear features may serve as valuable biomarkers for psychological disorders, reflecting the brain’s nonlinear dynamics. Although changes in EEG power spectral density are well-documented in BD patients, entropy measures offer a complementary nonlinear approach that is less explored. This study provides a comprehensive evaluation of various entropy measures, including approximate entropy, sample entropy, permutation entropy (PermEn), fuzzy entropy (FuzzyEn), and dispersion entropy (DispEn). Additionally, to capture the complexity of signal fluctuations at different scales, four multiscale entropies are evaluated to assess their effectiveness in distinguishing individuals with BD from healthy controls (HCs). We used resting-state EEG data from both eyes-closed and eyes-open (EO) conditions, collected from 60 individuals: 30 patients with BD (15 females, mean age =37.33, standard deviation (SD) =8.83) and 30 HC participants (13 females, mean age =35.73, SD =9.01). After preprocessing the raw EEG data, five basic and four multiscale entropy measures were calculated separately for the five cortical brain regions. Statistical analyses and machine learning approaches were then applied to assess the differences in entropy measures between the two groups. The results showed that BD patients exhibited higher irregularity in the frontal and central brain regions compared to HC participants across all basic entropy measures. The most significant changes were observed for DispEn and FuzzyEn measures. When considering multiscale entropies, the most significant results were found in the beta frequency band (approximately scale factor 4) in anterior brain regions, particularly for multiscale dispersion entropy (MDE). Furthermore, the best classification performance was achieved during the EO condition for the MDE measure, with an accuracy of 95%, specificity of 93.33%, and sensitivity of 96.66%. The study highlights alterations in entropy measures, particularly MDE in the frontal brain region, suggesting its potential as a sensitive biomarker for bipolar and related disorders.
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Bagheri et al. (2025) studied this question.
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