TY - GEN
T1 - Analysis of complexity based EEG features for the diagnosis of Alzheimer's disease
AU - Staudinger, Tyler
AU - Polikar, Robi
PY - 2011
Y1 - 2011
N2 - As life expectancy increases, particularly in the developed world, so does the prevalence of Alzheimer's Disease (AD). AD is a neurodegenerative disorder characterized by neu-rofibrillary plaques and tangles in the brain that leads to neu-ronal death and dementia. Early diagnosis of AD is still a major unresolved health concern: several biomarkers are being investigated, among which the electroencephalogram (EEG) provides the only option for an electrophysiological information. In this study, EEG signals obtained from 161 subjects 79 with AD, and 82 age-matched controls (CN) are analyzed using several nonlinear signal complexity measures. These measures include: Hi-guchi fractal dimension (HFD), spectral entropy (SE), spectral centroid (SC), spectral roll-off (SR), and zero-crossing rate (ZCR). HFD is a quantitative measure of time series complexity derived from fractal theory. Among spectral measures, SE measures the level of disorder in the spectrum, SC is a measure of spectral shape, and SR is frequency sample below which a specified percent of the spectral magnitude distribution is contained. Lastly, ZCR is simply the rate at which the signal changes signs. A t-test was first applied to determine those features that provide significant differences between the groups. Those features were then used to train a neural network. The classification accuracies ranged from 60-66%, suggesting they contain some discriminatory information; however, not enough to be clinically useful alone. Combining these features and training a support vector machine (SVM) resulted in a diagnostic accuracy of 78%, indicating that these feature carry complementary information.
AB - As life expectancy increases, particularly in the developed world, so does the prevalence of Alzheimer's Disease (AD). AD is a neurodegenerative disorder characterized by neu-rofibrillary plaques and tangles in the brain that leads to neu-ronal death and dementia. Early diagnosis of AD is still a major unresolved health concern: several biomarkers are being investigated, among which the electroencephalogram (EEG) provides the only option for an electrophysiological information. In this study, EEG signals obtained from 161 subjects 79 with AD, and 82 age-matched controls (CN) are analyzed using several nonlinear signal complexity measures. These measures include: Hi-guchi fractal dimension (HFD), spectral entropy (SE), spectral centroid (SC), spectral roll-off (SR), and zero-crossing rate (ZCR). HFD is a quantitative measure of time series complexity derived from fractal theory. Among spectral measures, SE measures the level of disorder in the spectrum, SC is a measure of spectral shape, and SR is frequency sample below which a specified percent of the spectral magnitude distribution is contained. Lastly, ZCR is simply the rate at which the signal changes signs. A t-test was first applied to determine those features that provide significant differences between the groups. Those features were then used to train a neural network. The classification accuracies ranged from 60-66%, suggesting they contain some discriminatory information; however, not enough to be clinically useful alone. Combining these features and training a support vector machine (SVM) resulted in a diagnostic accuracy of 78%, indicating that these feature carry complementary information.
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U2 - 10.1109/IEMBS.2011.6090374
DO - 10.1109/IEMBS.2011.6090374
M3 - Conference contribution
C2 - 22254735
AN - SCOPUS:84862262645
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2033
EP - 2036
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -