2.50
Hdl Handle:
http://hdl.handle.net/10755/156615
Type:
Presentation
Title:
Abnormal Electrophysiological Patterns of Dementia in Clinical EEG Data
Abstract:
Abnormal Electrophysiological Patterns of Dementia in Clinical EEG Data
Conference Sponsor:Sigma Theta Tau International
Conference Year:2005
Author:Holston, Ezra Charles, PhD, MSN, RN
P.I. Institution Name:The University of Iowa
Title:Post-doctoral Clinical Genetics Fellow and Research Scholar
A clinical diagnosis of Alzheimer's disease (AD) emerges at the mild-moderate stage of AD progression from a clinical history. Depressive symptoms, the most common co-morbidity of AD, can also be present. With quantitative electroencephalogram (qEEG), researchers have identified abnormal electrophysiological patterns in persons with AD. Recent qEEG research has described a specific abnormal electrophysiological pattern as an increase in theta frequency, occurring sequentially throughout the stages of AD. However, little is known about the abnormal electrophysiological patterns in persons diagnosed with AD and depressive co-morbidity. The purpose of this study is to characterize the electrophysiological patterns of patients diagnosed with dementia and depressive co-morbidity and to discuss how these electrophysiological patterns will affect evidence-based practice. This retrospective study has a descriptive, cross-sectional design. Sample include 21 community residents (aged 50-89 years) diagnosed with dementia of the Alzheimer type and manifested depressive symptoms. Electrophysiological data were extracted from the clinical EEG laboratory in a university-based medical center. The electrophysiological data were quantified with NeuroGuide. The 21 subjects (females = 12 (57%), males = 9 (43%)) had a mean age of 70.7 (SD 15). Nineteen (91%) were diagnosed with dementia and all manifested depressive symptoms. Clinical EEG recordings were analyzed, log transformed, quantified, and compared to a normative qEEG database to identify abnormal electrophysiological patterns indicative of dementia. Topographic brain images were used to visually display the topographic distribution of electrophysiological patterns. Statistical analyses will characterize the electrophysiological data. Descriptive analysis will be used to characterize the electrophysiological patterns. An ANOVA will be used to determine the group difference between gender and the effect of age. An evaluation of electrophysiological patterns may contribute to a more comprehensive assessment and accurate diagnosis of AD that will improve the evidence-based practice of health care for persons with AD and their caregivers.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleAbnormal Electrophysiological Patterns of Dementia in Clinical EEG Dataen_GB
dc.identifier.urihttp://hdl.handle.net/10755/156615-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Abnormal Electrophysiological Patterns of Dementia in Clinical EEG Data</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2005</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Holston, Ezra Charles, PhD, MSN, RN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">The University of Iowa</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Post-doctoral Clinical Genetics Fellow and Research Scholar</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">ezra-holston@uiowa.edu</td></tr><tr><td colspan="2" class="item-abstract">A clinical diagnosis of Alzheimer's disease (AD) emerges at the mild-moderate stage of AD progression from a clinical history. Depressive symptoms, the most common co-morbidity of AD, can also be present. With quantitative electroencephalogram (qEEG), researchers have identified abnormal electrophysiological patterns in persons with AD. Recent qEEG research has described a specific abnormal electrophysiological pattern as an increase in theta frequency, occurring sequentially throughout the stages of AD. However, little is known about the abnormal electrophysiological patterns in persons diagnosed with AD and depressive co-morbidity. The purpose of this study is to characterize the electrophysiological patterns of patients diagnosed with dementia and depressive co-morbidity and to discuss how these electrophysiological patterns will affect evidence-based practice. This retrospective study has a descriptive, cross-sectional design. Sample include 21 community residents (aged 50-89 years) diagnosed with dementia of the Alzheimer type and manifested depressive symptoms. Electrophysiological data were extracted from the clinical EEG laboratory in a university-based medical center. The electrophysiological data were quantified with NeuroGuide. The 21 subjects (females = 12 (57%), males = 9 (43%)) had a mean age of 70.7 (SD 15). Nineteen (91%) were diagnosed with dementia and all manifested depressive symptoms. Clinical EEG recordings were analyzed, log transformed, quantified, and compared to a normative qEEG database to identify abnormal electrophysiological patterns indicative of dementia. Topographic brain images were used to visually display the topographic distribution of electrophysiological patterns. Statistical analyses will characterize the electrophysiological data. Descriptive analysis will be used to characterize the electrophysiological patterns. An ANOVA will be used to determine the group difference between gender and the effect of age. An evaluation of electrophysiological patterns may contribute to a more comprehensive assessment and accurate diagnosis of AD that will improve the evidence-based practice of health care for persons with AD and their caregivers.</td></tr></table>en_GB
dc.date.available2011-10-26T14:57:23Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T14:57:23Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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