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Objective: To develop a machine learning (ML) methodology based on features extracted from odd-ball auditory evoked potentials to identify neurophysiologic changes induced by Clozapine (CLZ) treatment in responding schizophrenic (SCZ) subjects. This objective is of particular interest because CLZ, though a potentially dangerous drug, can be uniquely effective for otherwise medication-resistant SCZ...
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...
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)...
A model semi-metallic brake lining was subjected to full-scale automotive brake dynamometer tests. The structural properties and surface topography of brake linings were analyzed at different stages of wear testing and correlated to frictional performance. Characteristics of released wear particles were also addressed. A combination of abrasive and adhesive wear with oxidative processes dominated...
Despite increasing recognition of schizophrenia as a risk factor for diabetes, the prevalence and correlates of dysglycemia in people with schizophrenia have not been adequately studied. Discerning the modifiable risk factors is crucial for developing diabetes prevention strategies in schizophrenia.Socio-demographic, clinical and recent laboratory data were compiled from the case records and supplemental...
In this paper we present a simple and fast technique for correcting high amplitude artifacts that contaminate EEG signals. Examples of such artifacts are ocular movement, eye blinks, head movement, etc. Since the measured EEG data can be modeled as a linear combination of brain sources and artifacts, the proposed technique is based on multiplying the observed data matrix by a blocking matrix that...
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