Changes in EEG-derived entropy are associated with symptom improvement in major depressive disorder following electroconvulsive therapy.

BACKGROUND

Major Depressive Disorder (MDD) is associated with alterations in brain activity that are not fully captured by symptom-based diagnostic approaches. EEG-derived entropy measures have been proposed as potential biomarkers of depressive symptomatology, although findings remain mixed and longitudinal evidence using clinically feasible EEG setups is limited.

METHODS

Seventeen patients with MDD undergoing modified electroconvulsive therapy (mECT) and seventeen matched healthy controls were included. Resting-state EEG was recorded using a simplified two-electrode frontal montage prior to seizure induction. To maximize data usability, a signal patching procedure was applied to reconstruct segments affected by artifacts while preserving the fundamental properties of the original EEG signals. Entropy was computed across several frequency bands using a permutation entropy calculation with order 3. Changes in entropy between baseline and post-treatment were examined with respect to changes in depressive symptoms.

RESULTS

At baseline, patients exhibited higher EEG entropy than controls, particularly in the beta frequency band. Following mECT, patients showed a significant reduction in beta-band entropy. Reductions in left frontal beta-band entropy accounted for variance in symptom improvement.

CONCLUSIONS

Reductions in frontal beta-band entropy were associated with clinical improvement following mECT and may reflect normalization of cortical activity in MDD. The combination of entropy-based measures with signal patching may enhance the amount of useful information extracted from simplified EEG recordings, supporting their potential utility as biomarkers of treatment response in depression.