Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Joseph C. McBride; Xiaopeng Zhao; Nancy B. Munro; Charles D. Smith; Gregory A. Jicha; Lee Hively; Lucas S. Broster; Frederick A. Schmitt; Richard J. Kryscio; Yang Jiang

doi:10.1016/j.cmpb.2014.01.019

Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Joseph C. McBride
, Xiaopeng Zhao
, Nancy B. Munro
, Charles D. Smith
, Gregory A. Jicha
, Lee Hively
, Lucas S. Broster
, Frederick A. Schmitt
, Richard J. Kryscio
, Yang Jiang

Research output: Contribution to journal › Article › peer-review

172 Scopus citations

Abstract

Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.

Original language	English
Pages (from-to)	153-163
Number of pages	11
Journal	Computer Methods and Programs in Biomedicine
Volume	114
Issue number	2
DOIs	https://doi.org/10.1016/j.cmpb.2014.01.019
State	Published - Apr 2014

Bibliographical note

Funding Information:
Research was sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory , managed by UT-Battelle, LLC, for the US Department of Energy under Contract No. DE-AC05-00OR22725; by the NSF under grant numbers CMMI-0845753 and CMMI-1234155 ; and in part by the NIH under grants NIH P30 AG028383 to UK Sanders-Brown Center on Aging, NIH AG00986 to YJ, and NIH NCRR UL1RR033173 to UK Center for Clinical and Translational Science. The contributions to this paper by two of the authors (N. B. Munro and L. M. Hively) was prepared while acting in their own independent capacities and not on behalf of UT-Battelle, LLC, or its affiliates or successors, or Oak Ridge National Laboratory, or the US Department of Energy.

Funding

Research was sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory , managed by UT-Battelle, LLC, for the US Department of Energy under Contract No. DE-AC05-00OR22725; by the NSF under grant numbers CMMI-0845753 and CMMI-1234155 ; and in part by the NIH under grants NIH P30 AG028383 to UK Sanders-Brown Center on Aging, NIH AG00986 to YJ, and NIH NCRR UL1RR033173 to UK Center for Clinical and Translational Science. The contributions to this paper by two of the authors (N. B. Munro and L. M. Hively) was prepared while acting in their own independent capacities and not on behalf of UT-Battelle, LLC, or its affiliates or successors, or Oak Ridge National Laboratory, or the US Department of Energy.

Funders	Funder number
US Department of Energy	DE-AC05-00OR22725
National Science Foundation Arctic Social Science Program	CMMI-0845753, CMMI-1234155
National Science Foundation Arctic Social Science Program
National Institutes of Health (NIH)	P30 AG028383, UL1RR033173
National Institutes of Health (NIH)
National Institute on Aging	K01AG000986
National Institute on Aging
Oak Ridge National Laboratory

Keywords

EEG-based diagnosis
Early Alzheimer's disease
Entropy
Mild cognitive impairment
Spectral

ASJC Scopus subject areas

Software
Computer Science Applications
Health Informatics

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10.1016/j.cmpb.2014.01.019

Cite this

McBride, J. C., Zhao, X., Munro, N. B., Smith, C. D., Jicha, G. A., Hively, L., Broster, L. S., Schmitt, F. A., Kryscio, R. J., & Jiang, Y. (2014). Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease. Computer Methods and Programs in Biomedicine, 114(2), 153-163. https://doi.org/10.1016/j.cmpb.2014.01.019

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title = "Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease",

abstract = "Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3\%, 85.4\%, and 79.2\% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.",

keywords = "EEG-based diagnosis, Early Alzheimer's disease, Entropy, Mild cognitive impairment, Spectral",

author = "McBride, \{Joseph C.\} and Xiaopeng Zhao and Munro, \{Nancy B.\} and Smith, \{Charles D.\} and Jicha, \{Gregory A.\} and Lee Hively and Broster, \{Lucas S.\} and Schmitt, \{Frederick A.\} and Kryscio, \{Richard J.\} and Yang Jiang",

note = "Funding Information: Research was sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory , managed by UT-Battelle, LLC, for the US Department of Energy under Contract No. DE-AC05-00OR22725; by the NSF under grant numbers CMMI-0845753 and CMMI-1234155 ; and in part by the NIH under grants NIH P30 AG028383 to UK Sanders-Brown Center on Aging, NIH AG00986 to YJ, and NIH NCRR UL1RR033173 to UK Center for Clinical and Translational Science. The contributions to this paper by two of the authors (N. B. Munro and L. M. Hively) was prepared while acting in their own independent capacities and not on behalf of UT-Battelle, LLC, or its affiliates or successors, or Oak Ridge National Laboratory, or the US Department of Energy. ",

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AU - Zhao, Xiaopeng

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AU - Jicha, Gregory A.

AU - Hively, Lee

AU - Broster, Lucas S.

AU - Schmitt, Frederick A.

AU - Kryscio, Richard J.

AU - Jiang, Yang

N1 - Funding Information: Research was sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory , managed by UT-Battelle, LLC, for the US Department of Energy under Contract No. DE-AC05-00OR22725; by the NSF under grant numbers CMMI-0845753 and CMMI-1234155 ; and in part by the NIH under grants NIH P30 AG028383 to UK Sanders-Brown Center on Aging, NIH AG00986 to YJ, and NIH NCRR UL1RR033173 to UK Center for Clinical and Translational Science. The contributions to this paper by two of the authors (N. B. Munro and L. M. Hively) was prepared while acting in their own independent capacities and not on behalf of UT-Battelle, LLC, or its affiliates or successors, or Oak Ridge National Laboratory, or the US Department of Energy.

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N2 - Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.

AB - Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.

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KW - Entropy

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KW - Spectral

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Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this