TY - GEN
T1 - Scalp EEG signal reconstruction for detection of mild cognitive impairment and early Alzheimer's disease
AU - McBride, Joseph
AU - Zhao, Xiaopeng
AU - Munro, Nancy
AU - Jiang, Yang
AU - Smith, Charles
AU - Jicha, Gregory
PY - 2013
Y1 - 2013
N2 - Mild cognitive impairment (MCI) is a neurological disease which is often comorbid with early stages of Alzheimer's disease (AD). This study explores the potential for detecting changes in neurological functional organization which may be indicative of MCI and early AD using neural network models for scalp EEG signal reconstruction. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NC), 16 MCI, and 17 early-stage AD - are examined. Neural network models are trained to reconstruct artificially 'deleted' samples of EEG using subsets of records from NC participants. Models are applied to EEG records and quality scores are assigned to reconstructions of individual channels. Principal components of regional average reconstruction quality scores are used in a support vector machine model to discriminate between groups. Analyses demonstrate accuracies of 90.3% for MCI vs. NC (p-value<0.0005), 90.6% for AD vs. NC (p-value<0.0003), and 87.5% for AD/MCI vs. NC (p-value<0.0003). Techniques developed here may be used to detect changes in EEG activity due to neurological degeneration associated with MCI and early AD.
AB - Mild cognitive impairment (MCI) is a neurological disease which is often comorbid with early stages of Alzheimer's disease (AD). This study explores the potential for detecting changes in neurological functional organization which may be indicative of MCI and early AD using neural network models for scalp EEG signal reconstruction. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NC), 16 MCI, and 17 early-stage AD - are examined. Neural network models are trained to reconstruct artificially 'deleted' samples of EEG using subsets of records from NC participants. Models are applied to EEG records and quality scores are assigned to reconstructions of individual channels. Principal components of regional average reconstruction quality scores are used in a support vector machine model to discriminate between groups. Analyses demonstrate accuracies of 90.3% for MCI vs. NC (p-value<0.0005), 90.6% for AD vs. NC (p-value<0.0003), and 87.5% for AD/MCI vs. NC (p-value<0.0003). Techniques developed here may be used to detect changes in EEG activity due to neurological degeneration associated with MCI and early AD.
KW - Alzheimer's disease
KW - EEG
KW - mild cognitive impairment
KW - signal reconstruction
UR - http://www.scopus.com/inward/record.url?scp=84887771619&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887771619&partnerID=8YFLogxK
U2 - 10.1109/BSEC.2013.6618497
DO - 10.1109/BSEC.2013.6618497
M3 - Conference contribution
AN - SCOPUS:84887771619
SN - 9781479921188
T3 - Proceedings of the 2013 4th Annual ORNL Biomedical Sciences and Engineering Conference: Collaborative Biomedical Innovations, BSEC 2013
BT - Proceedings of the 2013 4th Annual ORNL Biomedical Sciences and Engineering Conference
T2 - 2013 4th Annual ORNL Biomedical Sciences and Engineering Conference: Collaborative Biomedical Innovations, BSEC 2013
Y2 - 21 May 2013 through 23 May 2013
ER -