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Journal of Medical Systems
Published in: Journal of Medical Systems 5/2016

01-05-2016 | Patient Facing Systems

An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer’s Disease

Authors: Hsiu-Sen Chiang, Shun-Chi Pao

Published in: Journal of Medical Systems | Issue 5/2016

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Abstract

Alzheimer’s disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early treatment of Alzheimer’s disease can significantly reduce deterioration. Early diagnosis is difficult, and early symptoms are frequently overlooked. While much of the literature focuses on disease detection, the use of electroencephalography (EEG) in Alzheimer’s diagnosis has received relatively little attention. This study combines the fuzzy and associative Petri net methodologies to develop a model for the effective and objective detection of Alzheimer’s disease. Differences in EEG patterns between normal subjects and Alzheimer patients are used to establish prediction criteria for Alzheimer’s disease, potentially providing physicians with a reference for early diagnosis, allowing for early action to delay the disease progression.
Literature
1.
Da Silva, S. A., Scazufca, M., and Menezes, P. R., Population impact of depression on functional disability in elderly: Results from “São Paulo Ageing & Health Study” (SPAH). Eur. Arch. Psychiatry Clin. Neurosci. 263(2):153–158, 2013.CrossRefPubMed
2.
Lin, R. T., Lai, C. L., Tai, C. T., Liu, C. K., Yen, Y. Y., and Howng, S. L., Prevalence and subtypes of dementia in southern Taiwan: Impact of age, sex, education, and urbanization. J. Neurol. Sci. 160(1):67–75, 1998.CrossRefPubMed
3.
Leon, J., Cheng, C. K., and Neumann, P. J., Alzheimer’s disease care: Costs and potential savings. Health Aff (Millwood). 17(6):206–216, 1998.CrossRefPubMed
4.
Babiloni, C., Frisoni, G. B., Pievani, M., et al., Hippocampal volume and cortical sources of EEG alpha rhythms in mild cognitive impairment and Alzheimer disease. NeuroImage 44(1):123–135, 2009.CrossRefPubMed
5.
Yang, A. C., Wang, S. J., Lai, K. L., et al., Cognitive and neuropsychiatric correlates of EEG dynamic complexity in patients with Alzheimer’s disease. Prog. Neuropsychopharmacol. Biol. Psychiatry 47:52–61, 2013.CrossRefPubMed
6.
McBride, J. C., Zhao, X., Munro, N. B., et al., Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer’s disease. Comput. Methods Prog. Biomed. 114(2):153–163, 2014.CrossRef
7.
Fu, K., Qu, J., Chai, Y., and Dong, Y., Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomed. Signal Process. Control 13:15–22, 2014.CrossRef
8.
Chen, C. C., Hsu, C. Y., Chiu, H. W., Hu, C. J., and Lee, T. C., Frequency power and coherence of electroencephalography are correlated with the severity of Alzheimer’s disease: A multicenter analysis in Taiwan. J. Formos. Med. Assoc. 114(8):729–735, 2015.CrossRefPubMed
9.
Waldemar, G., Dubois, B., Emre, M., et al., Recommendations for the diagnosis and management of Alzheimer’s disease and other disorders associated with dementia: EFNS guideline. Eur. J. Neurol. 14(1):e1–e26, 2007.CrossRefPubMed
10.
Christensen, M. D., and White, H. K., Dementia assessment and management. J. Am. Med. Dir. Assoc. 3(8):e89–e98, 2007.CrossRef
11.
Rabims, P. V., Lyketsos, C. G., and Steele, C. D., Practical Dementia Care, 2nd edition. University Press, Oxford, 2006.CrossRef
12.
Mann, D. M., The neuropathology of Alzheimer’s disease: A review with pathogenetic, aetiological and therapeutic considerations. Mech. Ageing Dev. 31(3):213–255, 1985.CrossRefPubMed
13.
Liu, H. C., Lin, K. N., Teng, E. L., et al., Prevalence and subtypes of dementia in Taiwan: A community survey of 5297 individuals. J. Am. Geriatr. Soc. 43(2):144–149, 1995.CrossRefPubMed
14.
Klich-Rączka, A., Piotrowicz, K., Mossakowska, M., et al., The assessment of cognitive impairment suspected of dementia in Polish elderly people: Results of the population-based PolSenior Study. Exp. Gerontol. 57:233–242, 2014.CrossRefPubMed
15.
Strand, B. H., Langballe, E. M., Hjellvik, V., et al., Midlife vascular risk factors and their association with dementia deaths: Results from a Norwegian prospective study followed up for 35years. J. Neurol. Sci. 324(1–2):124–130, 2013.CrossRefPubMed
16.
Staudinger, T., and Polikar, R., Analysis of complexity based EEG features for the diagnosis of Alzheimer’s disease. IEEE Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011:2033–2036, 2011.PubMed
17.
Kramer, G., van der Flier, W. M., de Langen, C., Blankenstein, M. A., Scheltens, P., and Stam, C. J., EEG functional connectivity and ApoE genotype in Alzheimer’s disease and controls. Clin. Neurophysiol. 119(12):2727–2732, 2008.CrossRefPubMed
18.
Chaudhry, M., Hasnain, S., Snitz, B. E., et al., Association of APOE polymorphisms and stressful life events with dementia in a Pakistani population. Neurosci. Lett. 570:42–46, 2014.CrossRefPubMedPubMedCentral
19.
Chu, C. S., Lu, T., Tsai, S. J., et al., APOE ε4 polymorphism and cognitive deficit among the very old Chinese veteran men without dementia. Neurosci. Lett. 576:17–21, 2014.CrossRefPubMed
20.
Berger, H., On the electroencephalogram of man. I. Arch. Psychiatr. Nervenkr. 87:527–570, 1929.CrossRef
21.
Wilson, W. P., Musella, L., and Short, M. J., The electroencephalogram in dementia. Contemp. Neurol. Ser. 15:205–221, 1977.PubMed
22.
Signorino, M., Pucci, E., Belardinelli, N., Nolle, G., and Angeleri, F., EEG spectral analysis in vascular and Alzheimer dementia. Electroencephalogr. Clin. Neurophysiol. 94(5):313–325, 1995.CrossRefPubMed
23.
Cantone, M., Di Pino, G., Capone, F., et al., The contribution of transcranial magnetic stimulation in the diagnosis and in the management of dementia. Clin. Neurophysiol. 125(8):1509–1532, 2014.CrossRefPubMed
24.
Del Felice, A., Broggio, E., Valbusa, V., Gambina, G., Arcaro, C., and Manganotti, P., Transient epileptic amnesia mistaken for mild cognitive impairment? A high-density EEG study. Epilepsy Behav. 36:41–46, 2014.CrossRefPubMed
25.
Soininen, H., Partanen, J. V., Puranen, M., et al., EEG and computed tomography in the investigation of patients with senile dementia. J. Neurol. Neurosurg. Psychiatry 45(8):711–714, 1982.CrossRefPubMedPubMedCentral
26.
Jelic, V., Shigeta, M., Julin, P., Almkvist, O., Winblad, B., and Wahlund, L. O., Quantitative electroencephalography power and coherence in Alzheimer’s disease and mild cognitive impairment. Dementia 7(6):314–323, 1996.PubMed
27.
Knott, V., Mohr, E., Mahoney, C., et al., Quantitative electroencephalography in Alzheimer’s disease: comparison with a control group, population norms and mental status. J. Psychiatry Neurosci. 26(2):106–116, 2001.PubMedPubMedCentral
28.
Moretti, D. V., Babiloni, C., Binetti, G., et al., Individual analysis of EEG frequency and band power in mild Alzheimer’s disease. Clin. Neurophysiol. 115(2):299–308, 2004.CrossRefPubMed
29.
Lehmanna, C., Koenig, T., Jelic, V., et al., Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). J. Neurosci. Methods 161(2):342–350, 2007.CrossRef
30.
Pucci, E., Belardinelli, N., CacchioÁ, G., Signorino, M., and Angeleri, F., EEG power spectrum differences in early and late onset forms of Alzheimer’s disease. Clin. Neurophysiol. 110(4):621–631, 1999.CrossRefPubMed
31.
Sankari, Z., Adeli, H., and Adeli, A., Intrahemispheric, interhemispheric, and distal EEG coherence in Alzheimer’s disease. Clin. Neurophysiol. 122(5):897–906, 2011.CrossRefPubMed
32.
Kikuchi, M., Wadac, Y., Takedaa, T., Oed, H., Hashimoto, T., and Koshino, Y., EEG harmonic responses to photic stimulation in normal aging and Alzheimer’s disease: Differences in interhemispheric coherence. Clin. Neurophysiol. 113(7):1045–1051, 2002.CrossRefPubMed
33.
Jeong, J., EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115(7):1490–1505, 2004.CrossRefPubMed
34.
Cooley, J. W., and Tukey, J. W., An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19(90):297–301, 1965.CrossRef
35.
Fox, L., and Parker, I. B., Chebyshev polynomials in numerical analysis. Oxford University Press, Oxford, London, 1968.
36.
37.
O’connor, D. W., Pollitt, P. A., Hyde, J. B., et al., The reliability and validity of the Mini-Mental State in a British community survey. J. Psychiatr. Res. 23(1):87–96, 1989.CrossRefPubMed
38.
Haddad, R. A., and Akansu, A. N., A class of fast gaussian binomial filters for speech and image processing. IEEE Trans. Acoust. Speech Signal Process. 39(3):723–727, 1991.CrossRef
39.
Ross, T. J., Fuzzy logic with engineering applications. Wiley, Hoboken, 2010.CrossRef
40.
Shih, D. H., Chiang, H. S., and Lin, B., A generalized associative petri net for reasoning. IEEE Trans. Knowl. Data Eng. 19(9):1241–1251, 2007.CrossRef
41.
Shih, D. H., Chiang, H. S., and Lin, B., Collaborative spam filtering with heterogeneous agents. Expert Syst. Appl. 35(4):1555–1566, 2008.CrossRef
42.
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In International joint conference on artificial intelligence. Proceedings of the 14th international joint conference on Artificial intelligence (IJCAI’95). 2:1137–1143, 1995.
43.
Polikar, R., Topalis, A., Parikh, D., et al., An ensemble based data fusion approach for early diagnosis of Alzheimer’s disease. Inf. Fusion 9(1):83–95, 2008.CrossRef
44.
Rossini, P. M., Buscema, M., Capriotti, M., et al., Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy? Clin. Neurophysiol. 119(7):1534–1545, 2008.CrossRefPubMed
Metadata
Title
An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer’s Disease
Authors
Hsiu-Sen Chiang
Shun-Chi Pao
Publication date
01-05-2016
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 5/2016
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-016-0476-7

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