Early EEG-Derived Seizure and Suppression Patterns During Electroconvulsive Therapy and Their Association with Early Antidepressant Response in Young Patients

Abstract

Early clinical response to electroconvulsive therapy (ECT) varies substantially across patients. The physiological expression of ECT-induced seizures, captured by routinely recorded EEG seizure duration and suppression indices together with autonomic responses, reflects interactions among cortical excitability, inhibitory processes, and peripheral activation. In younger patients, these systems may operate within a distinct physiological range, making early-session responses a particularly informative window for examining how seizure dynamics and cortical suppression relate to antidepressant effects, yet the clinical relevance of coordinated early physiological patterns remains unclear. We studied 28 young patients with major depressive disorder receiving ECT. Five routinely recorded physiological measures—stimulus dose, EEG seizure duration, EEG suppression index, systolic blood pressure change, and pulse rate change—were averaged across the first three ECT sessions and standardized. Unsupervised k-means clustering characterized early physiological response patterns, and exploratory logistic regression examined associations with early clinical improvement, defined as a ≥ 5-point reduction in HAMD-17 score by the third session. Two early physiological patterns were identified. A pattern characterized by longer seizure duration and lower EEG suppression was associated with greater early symptom improvement, despite heterogeneous autonomic responses. In regression analyses, seizure duration was positively associated with improvement, whereas EEG suppression index showed a strong negative association. Collectively, early physiological features showed clear within-sample separation between patients with and without early improvement (apparent AUC = 0.86). These findings suggest that early ECT physiology in young patients reflects coordinated response states rather than isolated markers, highlighting the potential value of multivariate interpretation of routine EEG-derived indices.