Using Pre-treatment EEG Data to Predict Response to SSRI Treatment for MDD

被引:18
作者
Khodayari-Rostamabad, Ahmad [1 ]
Reilly, James P. [1 ]
Hasey, Gary [2 ,3 ]
deBruin, Hubert [1 ]
MacCrimmon, Duncan [2 ,3 ]
机构
[1] McMaster Univ, Dept Elect & Comp Engn, Hamilton, ON L8S 4K1, Canada
[2] McMaster Univ, Dept Psychiat & Behav Neurosci, Hamilton, ON L8S 4K1, Canada
[3] St Joseph Hosp, Mood Disorders Program, Hamilton, ON L8S 4K1, Canada
来源
2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2010年
关键词
MUTUAL INFORMATION; MAJOR DEPRESSION; ANTIDEPRESSANT; SELECTION; OUTCOMES; MODEL;
D O I
10.1109/IEMBS.2010.5627823
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure. The classifier was realized as a kernelized partial least squares regression procedure, whose output is the predicted response. A low-dimensional kernelized principal component representation of the feature space was used for the purposes of visualization and clustering analysis. The overall method was evaluated using an 11-fold nested cross-validation procedure for which over 85% average prediction performance is obtained. The results indicate that ML methods hold considerable promise in predicting the efficacy of SSRI antidepressant therapy for major depression.
引用
收藏
页码:6103 / 6106
页数:4
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