一种使用脑信号和机器学习的用于抑郁症治疗的智能闭环经颅电刺激系统。
An intelligent closed-loop CES for depression treatment using brain signals and machine learning.
作者信息
Mosayebinejad Fahimeh, Mirhosseini Hamid, Jambarsang Sara, Saeed Fahimeh, Daneshmand Reza, Mojaver Morteza, Zargar Setareh, Khosravi Najafabadi Asma
机构信息
Research Assistant, Shahid Sadoughi University of Medical Sciences, Yazd, , Iran.
Associate Professor, Department of psychiatry, Research center of addiction and behavioral sciences, Non-communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
出版信息
MethodsX. 2025 Aug 19;15:103575. doi: 10.1016/j.mex.2025.103575. eCollection 2025 Dec.
This method presents a double-blind, randomized, parallel-group clinical trial aimed at personalizing cranial electrotherapy stimulation (CES) according to individual brain signals, assessing the effect of Intelligent Closed-Loop Cranial Electrotherapy Stimulation (IC-CES) on Major Depressive Disorder (MDD). To evaluate the effectiveness of IC-CES in treating MDD. 120 participants were randomly assigned to either the IC-CES group or the CES group. Depression, anxiety, and sleep quality were measured at multiple time points using standard questionnaires.
该方法开展了一项双盲、随机、平行组临床试验，旨在根据个体脑信号对颅电刺激（CES）进行个性化调整，评估智能闭环颅电刺激（IC-CES）对重度抑郁症（MDD）的疗效。为评估IC-CES治疗MDD的有效性，120名参与者被随机分配至IC-CES组或CES组。使用标准问卷在多个时间点测量抑郁、焦虑和睡眠质量。
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