Alzheimer's disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer's disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer's disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer's disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer's disease from EEG records.

Department of Computer Science Faculty of Science University of South Bohemia in Ceske Budejovice Ceske Budejovice Czech Republic

Department of Instrumentation and Control Engineering Faculty of Mechanical Engineering Czech Technical University Prague Technicka Street 4 16607 Prague 6 Czech Republic

Department of Neurology Faculty of Medicine in University Hospital Hradec Králové Charles University Prague Sokolská 581 500 05 Hradec Králové Czech Republic

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Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, Bakardjian H, Benali H, Bertram L, Blennow K, et al. Preclinical alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12(3):292–323. doi: 10.1016/j.jalz.2016.02.002. PubMed DOI PMC

He Y, Chen Z, Gong G, Evans A. Neuronal networks in alzheimers disease. Neuroscientist. 2009;15(4):333–350. doi: 10.1177/1073858409334423. PubMed DOI

Morrison JH, Scherr S, Lewis DA, Campbell M, Bloom FE, Rogers J, Benoit R (1986) The laminar and regional distribution of neocortical somatostatin and neuritic plaques: implications for alzheimer’s disease as a global neocortical disconnection syndrome. Biol Substrates Alzheimers Dis :115–131

Sorg C, Riedl V, Mühlau M, Calhoun VD, Eichele T, Läer L, Drzezga A, Förstl H, Kurz A, Zimmer C, et al. Selective changes of resting-state networks in individuals at risk for alzheimer’s disease. Proc Natl Acad Sci. 2007;104(47):18760–18765. doi: 10.1073/pnas.0708803104. PubMed DOI PMC

Palesi F, Castellazzi G, Casiraghi L, Sinforiani E, Vitali P, Gandini Wheeler-Kingshott CA, D’Angelo E. Exploring patterns of alteration in alzheimer’s disease brain networks: a combined structural and functional connectomics analysis. Front Neurosci. 2016;10:380. doi: 10.3389/fnins.2016.00380. PubMed DOI PMC

Stam CJ, Reijneveld JC. Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed Phys. 2007;1(1):1–19. doi: 10.1186/1753-4631-1-3. PubMed DOI PMC

Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, Rombouts SA, Maris E, Barkhof F, Scheltens P, Stam CJ. Loss of ’small-world’networks in alzheimer’s disease: graph analysis of fmri resting-state functional connectivity. PloS ONE. 2010;5(11):e13788. doi: 10.1371/journal.pone.0013788. PubMed DOI PMC

Adler G, Brassen S, Jajcevic A. Eeg coherence in alzheimer’s dementia. J Neural Transm. 2003;110(9):1051–1058. doi: 10.1007/s00702-003-0024-8. PubMed DOI

Jelic V, Johansson S-E, Almkvist O, Shigeta M, Julin P, Nordberg A, Winblad B, Wahlund L-O. Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of alzheimer’s disease. Neurobiol Aging. 2000;21(4):533–540. doi: 10.1016/S0197-4580(00)00153-6. PubMed DOI

Claus J, Kwa V, Teunisse S, Gérard J, Van Gool W, Hans J, Koelman T, Bour L, De Ongerboer Visser B. Slowing on quantitative spectral eeg is a marker for rate of subsequent cognitive and functional decline in early alzheimer disease. Alzheimer Dis Assoc Disord. 1998;12(3):167–174. doi: 10.1097/00002093-199809000-00008. PubMed DOI

Coben L, Chi D, Snyder A, Storandt M. Replication of a study of frequency analysis of the resting awake eeg in mild probabke alzheimer’s disease. Electroencephalogr Clin Neurophysiol. 1990;75(3):148–154. doi: 10.1016/0013-4694(90)90168-J. PubMed DOI

Duffy F, Albert M, McAnulty G. Brain electrical activity in patients with presenile and senile dementia of the alzheimer type. Ann Neurol. 1984;16(4):439–448. doi: 10.1002/ana.410160404. PubMed DOI

Ihl R, Dierks T, Martin E-M, Frölich L, Maurer K. Topography of the maximum of the amplitude of eeg frequency bands in dementia of the alzheimer type. Biol Psychiatry. 1996;39(5):319–325. doi: 10.1016/0006-3223(95)00174-3. PubMed DOI

Dauwels J, Vialatte F, Cichocki A. Diagnosis of alzheimer’s disease from eeg signals: where are we standing? Curr Alzheimer Res. 2010;7(6):487–505. doi: 10.2174/156720510792231720. PubMed DOI

Klimesch W. Eeg alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999;29(2-3):169–195. doi: 10.1016/S0165-0173(98)00056-3. PubMed DOI

Fernández A, Arrazola J, Maestú F, Amo C, Gil-Gregorio P, Wienbruch C, Ortiz T. Correlations of hippocampal atrophy and focal low-frequency magnetic activity in alzheimer disease: volumetric mr imaging-magnetoencephalographic study. Am J Neuroradiol. 2003;24(3):481–487. PubMed PMC

Helkala E-L, Hänninen T, Hallikainen M, Könönen M, Laakso M, Hartikainen P, Soininen H, Partanen J, Partanen K, Vainio P, et al. Slow-wave activity in the spectral analysis of the electroencephalogram and volumes of hippocampus in subgroups of alzheimer’s disease patients. Behav Neurosci. 1996;110(6):1235. doi: 10.1037/0735-7044.110.6.1235. PubMed DOI

Association A, et al. 2016 alzheimer’s disease facts and figures. Alzheimers Dement. 2016;12(4):459–509. doi: 10.1016/j.jalz.2016.03.001. PubMed DOI

Staudinger T, Polikar R (2011) Analysis of complexity based eeg features for the diagnosis of alzheimer’s disease. In: Engineering in medicine and biology society, EMBC, 2011 Annual international conference of the IEEE. IEEE, pp 2033–2036 PubMed

Stevens A, Kircher T. Cognitive decline unlike normal aging is associated with alterations of eeg temporo-spatial characteristics. Eur Arch Psychiatry Clin Neurosci. 1998;248(5):259–266. doi: 10.1007/s004060050047. PubMed DOI

Elgendi M, Vialatte F, Cichocki A, Latchoumane C, Jeong J, Dauwels J (2011) Optimization of eeg frequency bands for improved diagnosis of alzheimer disease. In: Engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE. IEEE, pp 6087–6091 PubMed

Strik WK, Chiaramonti R, Muscas GC, Paganini M, Mueller TJ, Fallgatter AJ, Versari A, Zappoli R. Decreased eeg microstate duration and anteriorisation of the brain electrical fields in mild and moderate dementia of the alzheimer type. Psychiatry Res Neuroimaging. 1997;75(3):183–191. doi: 10.1016/S0925-4927(97)00054-1. PubMed DOI

Müller T, Thome J, Chiaramonti R, Dierks T, Maurer K, Fallgatter A, Frölich L, Scheubeck M, Strik W. A comparison of geeg and hmpao-spect in relation to the clinical severity of alzheimer’s disease. Eur Arch Psychiatry Clin Neurosci. 1997;247(5):259–263. doi: 10.1007/BF02900304. PubMed DOI

Akrofi K, Baker MC, O’Boyle MW, Schiffer RB (2008) Clustering and modeling of eeg coherence features of alzheimer’s and mild cognitive impairment patients. In: Engineering in medicine and biology society, 2008. EMBS 2008. 30th Annual international conference of the IEEE. IEEE, pp 1092–1095 PubMed

de Waal H, Stam CJ, de Haan W, van Straaten EC, Scheltens P, van der Flier WM. Young alzheimer patients show distinct regional changes of oscillatory brain dynamics. Neurobiol Aging. 2012;33(5):1008–e25. PubMed

Iznak A, Kolykhalov I, Zhygulskaya S, Vasilieva A, Selezneva A, Selezneva N. The quantitative eeg in early and differential diagnosis of mild dementia of different genesis. Eur Neuropsychopharmacol. 1998;8:S277–S278. doi: 10.1016/S0924-977X(98)80524-5. DOI

Henderson G, Ifeachor E, Hudson N, Goh C, Outram N, Wimalaratna S, Del Percio C, Vecchio F. Development and assessment of methods for detecting dementia using the human electroencephalogram. IEEE Trans Biomed Eng. 2006;53(8):1557–1568. doi: 10.1109/TBME.2006.878067. PubMed DOI

Lehmann C, Koenig T, Jelic V, Prichep L, John RE, Wahlund L-O, Dodge Y, Dierks T. Application and comparison of classification algorithms for recognition of alzheimer’s disease in electrical brain activity (eeg) J Neurosci Methods. 2007;161(2):342–350. doi: 10.1016/j.jneumeth.2006.10.023. PubMed DOI

Herrmann W, Fichte K, Freund G, et al. Reflections on the topics: Eeg frequency bands and regulation of vigilance. Pharmacopsychiatry. 1979;12(02):237–245. doi: 10.1055/s-0028-1094615. PubMed DOI

Morabito FC, Campolo M, Ieracitano C, Ebadi JM, Bonanno L, Bramanti A, Desalvo S, Mammone N, Bramanti P (2016) Deep convolutional neural networks for classification of mild cognitive impaired and alzheimer’s disease patients from scalp eeg recordings. In: 2016 IEEE 2nd International Forum on Research and technologies for society and industry leveraging a better tomorrow (RTSI). IEEE, pp 1–6

Cejnek M, Beneš PM, Bukovsky I (2014) Another adaptive approach to novelty detection in time series

Cejnek M, Bukovsky I. Concept drift robust adaptive novelty detection for data streams. Neurocomputing. 2018;309:46–53. doi: 10.1016/j.neucom.2018.04.069. DOI

Cao Y, Cai L, Wang J, Wang R, Yu H, Cao Y, Liu J. Characterization of complexity in the electroencephalograph activity of Alzheimer’s disease based on fuzzy entropy. Chaos Interdiscip J Nonlinear Sci. 2015;25(8):083116. doi: 10.1063/1.4929148. PubMed DOI

Deng B, Liang L, Li S, Wang R, Yu H, Wang J, Wei X. Complexity extraction of electroencephalograms in alzheimer’s disease with weighted-permutation entropy. Chaos Interdiscip J Nonlinear Sci. 2015;25(4):043105. doi: 10.1063/1.4917013. PubMed DOI

Cejnek M, Bukovsky I, Vysata O (2015) Adaptive classification of eeg for dementia diagnosis. In: 2015 International workshop on IEEE computational intelligence for multimedia Understanding (IWCIM), pp 1–5

Bishop CM (1994) Novelty detection and neural network validation. In: IEE Proceedings vision, image and signal processing, vol 141. IET, pp 217–222

Williams G, Baxter R, He H, Hawkins S, Gu L (2002) A comparative study of rnn for outlier detection in data mining. In: Null. IEEE, p 709

Bukovsky I, Oswald C, Cejnek M, Benes PM (2014) Learning entropy for novelty detection a cognitive approach for adaptive filters. In: Sensor signal processing for defence (SSPD) 2014, pp 1–5

McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, et al. The diagnosis of dementia due to alzheimer’s disease: Recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for alzheimer’s disease. Alzheimers Dement. 2011;7(3):263–269. doi: 10.1016/j.jalz.2011.03.005. PubMed DOI PMC

Morris JC. Revised criteria for mild cognitive impairment may compromise the diagnosis of alzheimer disease dementia. Arch Neurol. 2012;69(6):700–708. doi: 10.1001/archneurol.2011.3152. PubMed DOI PMC

Gupta M. Static and dynamic neural networks: from fundamentals to advanced theory. New York: Wiley; 2003.

Gupta M, Bukovsky I, Homma N, Solo AMG, Hou Z-G (2013) Fundamentals of higher order neural networks for modeling and simulation. In: Fundamentals of higher order neural networks for modeling and simulation. IGI Global, pp 103–133

Bukovskỳ I, Rodriguez R, Bila J, Homma N (2012) Prospects of gradient methods for nonlinear control, Automatizácia a riadenie v teórii a praxi ARTEP 2012

Widrow B. Adaptive signal processing, ser. Prentice-Hall signal processing series. Englewood Cliffs: Prentice-Hall; 1985.

Mandic DP, Goh VSL. Complex valued nonlinear adaptive filters: Noncircularity, Widely linear and neural models. New York: John Wiley & Sons; 2009.

Patel KP, Wymer DT, Bhatia VK, Duara R, Rajadhyaksha CD. Multimodality imaging of dementia: Clinical importance and role of integrated anatomic and molecular imaging. RadioGraphics. 2020;40(1):200–222. doi: 10.1148/rg.2020190070. PubMed DOI PMC

Hyman BT, Trojanowski JQ. Editorial on consensus recommendations for the postmortem diagnosis of alzheimer disease from the national institute on aging and the reagan institute working group on diagnostic criteria for the neuropathological assessment of alzheimer disease. J Neuropathol Exp Neurol. 1997;56(10):1095–1097. doi: 10.1097/00005072-199710000-00002. PubMed DOI

Zaborszky L, Pang K, Somogyi J, Nadasdy Z, Kallo I. The basal forebrain corticopetal system revisited. Ann N Y Acad Sci. 1999;877(1):339–367. doi: 10.1111/j.1749-6632.1999.tb09276.x. PubMed DOI

Fuller P, Sherman D, Pedersen NP, Saper CB, Lu J. Reassessment of the structural basis of the ascending arousal system. J Comp Neurol. 2011;519(5):933–956. doi: 10.1002/cne.22559. PubMed DOI PMC

Berntson G, Shafi R, Sarter M. Specific contributions of the basal forebrain corticopetal cholinergic system to electroencephalographic activity and sleep/waking behaviour. Eur J Neurosc. 2002;16(12):2453–2461. doi: 10.1046/j.1460-9568.2002.02310.x. PubMed DOI

Vyšata O, Procházka A, Mareš J, Rusina R, Pazdera L, Vališ M, Kukal J. Change in the characteristics of eeg color noise in alzheimer’s disease. Clin EEG Neurosci. 2014;45(3):147–151. doi: 10.1177/1550059413491558. PubMed DOI

Mizuno T, Takahashi T, Cho RY, Kikuchi M, Murata T, Takahashi K, Wada Y. Assessment of eeg dynamical complexity in alzheimer’s disease using multiscale entropy. Clin Neurophysiol. 2010;121(9):1438–1446. doi: 10.1016/j.clinph.2010.03.025. PubMed DOI PMC

Dauwels J, Vialatte F, Musha T, Cichocki A. A comparative study of synchrony measures for the early diagnosis of alzheimer’s disease based on eeg. Neuroimage. 2010;49(1):668–693. doi: 10.1016/j.neuroimage.2009.06.056. PubMed DOI