Khodayari-Rostamabad Ahmad, Reilly James P, Hasey Gary M, deBruin Hubert, MacCrimmon Duncan
Electrical and Computer Engineering Department, McMaster University, Hamilton, ON L8S 4K1, Canada.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:6418-21. doi: 10.1109/IEMBS.2011.6091584.
We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes. A leave-2-out (L2O) cross-validation procedure is used to evaluate the prediction performance. This pilot study involves 27 subjects who received either left high-frequency (HF) active rTMS therapy or simultaneous left HF and right low-frequency active rTMS therapy. Our results indicate that it is possible to predict rTMS treatment efficacy of either treatment modality with a specificity of 83% and a sensitivity of 78%, for a combined accuracy of 80%.
我们研究基于治疗前脑电图（EEG）的机器学习方法，以预测重复经颅磁刺激（rTMS）的治疗反应，rTMS是一种治疗重度抑郁症（MDD）的非药物治疗形式。学习过程包括从EEG数据中提取大量候选特征，从中选择一小部分统计学上最相关的特征进行进一步处理。基于因子分析混合（MFA）模型构建统计预测模型，该模型根据训练集将各个受试者分为反应者和无反应者类别。采用留二法（L2O）交叉验证程序评估预测性能。这项初步研究涉及27名受试者，他们接受了左侧高频（HF）主动rTMS治疗或同时接受左侧HF和右侧低频主动rTMS治疗。我们的结果表明，对于两种治疗方式中的任何一种，都有可能预测rTMS治疗效果，特异性为83%，敏感性为78%，综合准确率为80%。
