Preliminary Assessment of Intrahemispheric QEEG Measures in Bipolar Mood Disorders

Oluboka, O. J.; Stewart, Stacy; Sharma, Verinder; Mazmanian, Dwight; Persad, Emmanuel

doi:10.1177/070674370204700408

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…While both the BP and FD groups had significantly lower right and left composite scores compared to controls, only the BP group demonstrated poorer performance on the right versus left hemisphere tasks, and this occurred only for the composite score reflecting higher‐order cognitive processes, and not simple motor function. Several prior studies have also reported right hemisphere dysfunction using neuropsychological, electrophysiological and functional neuroimaging methods (11, 58, 82, 83). In one of the few at‐risk studies, Pierson et al.…”

Section: Discussionmentioning

confidence: 99%

Neurocognitive endophenotypes for bipolar disorder

Frantom¹,

Allen²,

Cross

2008

Bipolar Disorders

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Section: Discussionmentioning

confidence: 99%

Neurocognitive endophenotypes for bipolar disorder

Frantom¹,

Allen²,

Cross

2008

Bipolar Disorders

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show abstract

“…Other studies on brain electrical activity in BD used different protocols and/or different analyses such as event‐related designs and synchronization or coherence measures (e.g., ). For example, the analyses of Özerdem et al.…”

mentioning

confidence: 99%

Brain electrical source imaging in manic and depressive episodes of bipolar disorder

et al. 2014

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The spatial organization of the brain's electrical oscillations differed in patients with BD between manic and depressive mood states. The brain areas implementing the main functions that show opposing abnormalities during manic and depressive episodes were affected by unduly increased or decreased activity (beta or theta). The discussion considers that facilitating (beta) or inhibiting (theta) electrical activity can in either case result in behavioral facilitation or inhibition, depending on the function of the brain area.

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“…Their findings showed more disorganization in the right anterior hemisphere in BMD patients compared to schizophrenic ones. Moreover, a significant positive correlation between EEG features and the presence of family history was observed in the BMD group [2].…”

Section: Introductionmentioning

confidence: 79%

“…To classify mental disorders using EEGs, Oluboka et al [2] extracted the absolute power and coherence difference factors from EEG signals, as discriminative features to classify patients with BMD (type I) from schizophrenic patients. Their findings showed more disorganization in the right anterior hemisphere in BMD patients compared to schizophrenic ones.…”

Section: Introductionmentioning

confidence: 99%

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients

2010

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There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

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Preliminary Assessment of Intrahemispheric QEEG Measures in Bipolar Mood Disorders

Cited by 10 publications

References 18 publications

Neurocognitive endophenotypes for bipolar disorder

Neurocognitive endophenotypes for bipolar disorder

Brain electrical source imaging in manic and depressive episodes of bipolar disorder

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients

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