Abstract
To assist effective and precise diagnosis for mild cognitive impairment (MCI) and Alzheimer’s disease (AD), Electroencephalograph (EEG) has been widely used in clinical research of patients with AD at MCI state. To study the linear and nonlinear abnormality of EEG in AD and MCI patients, multiple characteristics was applied to distinguish AD and MCI patients from the normal controls (NC). EEG signals was recorded from 28 subjects, including 10 AD patients, 8 MCI subjects and 10 healthy elderly people. EEG signals in all channels was computed by auto-regressive model and multi scale entropy (MSE) to obtain relative power spectral density (PSD) value of each frequency band and entropy value in different time scales. Area under Receiver operating characteristic curve (AUC) was used to compare the classification ability of the two method. The ratio Alpha/theta of MCI group in left frontal area can distinguish MCI from NC subjects. Also the long scale entropy value in left frontal-central area manifests a better accuracy in distinguish AD and MCI from NC group. In addition, the combined feature from alpha/theta and long scale entropy in the left frontal central area can discriminate AD from NC group with higher AUC reaching 0.89. This indicated that combined PSD and MSE can be taken as a potential measure to detect AD in early state.
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References
Oulhaj A, Wilcock G K, Smith A D, et al. Predicting the time of conversion to MCI in the elderly: Role of verbal expression and learning [J]. Neurology, 73(73), 1436–42 (2009).
Levey A, Lah J: Mild cognitive impairment: An opportunity to identify patients at high risk for progression to Alzheimer’s disease. Clin Ther, 28(7), 991–1001 (2006).
Serrao V T, Brucki S M D, Campanholo K R, et al. Performance of a sample of patients with Mild Cognitive Impairment (MCI), Alzheimer’s Disease (AD) and healthy elderly on a lexical decision test (LDT) as a measure of pre-morbid intelligence [J]. Dementia E Neuropsychologia, 9(3), 265–269 (2015).
Hua X, Leow A D, Parikshak N, et al. Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease: an MRI study of 676 AD, MCI, and normal subjects [J]. Neuroimage, 43(3), 458–469 (2011).
Cabral C, Morgado P M, Campos C D, et al. Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages [J]. Computers in Biology & Medicine, 58, 101–109 (2015).
Poil SS, De HW, Wm V D F, et al. Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage [J]. Frontiers in Aging Neuroscience, 5(2), 99–103 (2013).
Jeong J: EEG dynamics in patients with Alzheimer’s disease. Clinical neurophysiology, 115(7), 1490–1505 (2004).
Babiloni C, Frisoni GB, Pievani M, et al: Hippocampal volume and cortical sources of EEG alpha rhythms in mild cognitive impairment and Alzheimer disease.[J]. NeuroImage, 44(1), 123–135 (2009).
Moretti D V, Miniussi C, Frisoni G B, et al. Hippocampal atrophy and EEG markers in subjects with mild cognitive impairment.[J]. Clinical Neurophysiology, 118(12), 2716–29 (2007).
Liu X, Zhang C, Ji Z, et al. Multiple characteristics analysis of Alzheimer’s electroencephalogram by power spectral density and Lempel-Ziv complexity [J]. Cognitive Neurodynamics, 10(2), 1–13 (2016).
Abasolo D, Hornero R, Espino P, et al.: Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with Approximate Entropy. Clinical Neurophysiology, 116(8):1826–1834 (2005).
Hogan MJ, Kilmartin L, Keane M, et al.: Electrophysiological entropy in younger adults, older controls and older cognitively declined adults. Brain research, 1445, 1–10 (2012).
Mizuno T, Takahashi T, Cho RY, et al.: Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy. Clinical Neurophysiology, 121(9), 1438–1446 (2010).
Acknowledgements
This work was supported by the National Science and Technology Ministry of Science and Technology Support Program (Grant No.2015BAI06B02) and the National High Technology Research and Development Program of China (Grant No.2015AA042304).
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Chai, X., Weng, X., Zhang, Z., Lu, Y., Liu, G., Niu, H. (2019). Quantitative EEG in Mild Cognitive Impairment and Alzheimer’s Disease by AR-Spectral and Multi-scale Entropy Analysis. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/2. Springer, Singapore. https://doi.org/10.1007/978-981-10-9038-7_29
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DOI: https://doi.org/10.1007/978-981-10-9038-7_29
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