Publications (12) View all
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Conference Proceeding: A machine learning approach using P300 responses to investigate effect of clozapine therapy
Engineering in Medicine and Biology Society (EMBC); 08/2012 -
Conference Proceeding: Using pre-treatment EEG data to predict response to SSRI treatment for MDD
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ABSTRACT: 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.Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010 -
Conference Proceeding: Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model
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ABSTRACT: An automated diagnosis procedure based on a statistical machine learning methodology using electroencephalograph (EEG) data is proposed for diagnosis of psychiatric illness. First, a large collection of candidate features, mostly consisting of various statistical quantities, are calculated from the subject's EEG. This large set of candidate features is then reduced into a much smaller set of most relevant features using a feature selection procedure. The selected features are then used to evaluate the class likelihoods, through the use of a mixture of factor analysis (MFA) statistical model. In a training set of 207 subjects, including 64 subjects with major depressive disorder (MDD), 40 subjects with chronic schizophrenia, 12 subjects with bipolar depression and 91 normal or healthy subjects, the average correct diagnosis rate attained using the proposed method is over 85%, as determined by various cross-validation experiments. The promise is that, with further development, the proposed methodology could serve as a valuable adjunctive tool for the medical practitioner.Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010 -
Article: Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression.
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ABSTRACT: 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%.Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:6418-21. -
Article: A machine learning approach for distinguishing age of infants using auditory evoked potentials.
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ABSTRACT: To develop a high performance machine learning (ML) approach for predicting the age and consequently the state of brain development of infants, based on their event related potentials (ERPs) in response to an auditory stimulus. The ERP responses of twenty-nine 6-month-olds, nineteen 12-month-olds and 10 adults to an auditory stimulus were derived from electroencephalogram (EEG) recordings. The most relevant wavelet coefficients corresponding to the first- and second-order moment sequences of the ERP signals were then identified using a feature selection scheme that made no a priori assumptions about the features of interest. These features are then fed into a classifier for determination of age group. We verified that ERP data could yield features that discriminate the age group of individual subjects with high reliability. A low dimensional representation of the selected feature vectors show significant clustering behavior corresponding to the subject age group. The performance of the proposed age group prediction scheme was evaluated using the leave-one-out cross validation method and found to exceed 90% accuracy. This study indicates that ERP responses to an acoustic stimulus can be used to predict the age and consequently the state of brain development of infants. This study is of fundamental scientific significance in demonstrating that a machine classification algorithm with no a priori assumptions can classify ERP responses according to age and with further work, potentially provide useful clues in the understanding of the development of the human brain. A potential clinical use for the proposed methodology is the identification of developmental delay: an abnormal condition may be suspected if the age estimated by the proposed technique is significantly less than the chronological age of the subject.Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 05/2011; 122(11):2139-50. · 3.12 Impact Factor
