Introduction: It is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia problems under world-wide rapid increasing of patients with dementia. In this sense, electroencephalography (EEG) has been utilized as a promising examination to screen and assist in diagnosing dementia, with advantages of sensitiveness to neural functions, inexpensiveness, and high availability. Moreover, the algorithm-based deep learning can expand EEG applicability, yielding accurate and automatic classification easily applied even in general hospitals without any research specialist. Methods: We utilized a novel deep neural network, with which high accuracy of discrimination was archived in neurological disorders in the previous study. Based on this network, we analyzed EEG data of healthy volunteers (HVs, N = 55), patients with Alzheimer’s disease (AD, N = 101), dementia with Lewy bodies (DLB, N = 75), and idiopathic normal pressure hydrocephalus (iNPH, N = 60) to evaluate the discriminative accuracy of these diseases. Results: High discriminative accuracies were archived between HV and patients with dementia, yielding 81.7% (vs. AD), 93.9% (vs. DLB), 93.1% (vs. iNPH), and 87.7% (vs. AD, DLB, and iNPH). Conclusion: This study revealed that the EEG data of patients with dementia were successfully discriminated from HVs based on a novel deep learning algorithm, which could be useful for automatic screening and assisting diagnosis of dementia diseases.

Alzhemer’s disease and international. World alzheimer report; 2019.
Taylor JP, McKeith IG, Burn DJ. New evidence on the management of Lewy body dementia. Lancet Neurol. 2020;19:157–69.
Ieracitano C, Mammone N, Hussain A, Morabito FC. A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw. 2020;123:176–90.
McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor JP, Weintraub D, et al. Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB consortium. Neurology. 2017;89(1):88–100.
Garn H, Coronel C, Waser M, Caravias G, Ransmayr G. Differential diagnosis between patients with probable Alzheimer’s disease, Parkinson’s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features. J Neural Transm. 2017;124(5):569–81.
Griffa A, Van De Ville D, Herrmann FR, Allali G. Neural circuits of idiopathic Normal Pressure Hydrocephalus: a perspective review of brain connectivity and symptoms meta-analysis. Neurosci Biobehav Rev. 2020;112:452–71.
Mori E, Ishikawa M, Kato T, Kazui H, Miyake H, Miyajima M, et al. Guidelines for management of idiopathic normal pressure hydrocephalus: second edition. Neurol Med Chir. 2012;52(11):775–809.
Jaraj D, Rabiei K, Marlow T, Jensen C, Skoog I, Wikkelso C, et al. Prevalence of idiopathic normal-pressure hydrocephalus. Neurology. 2014;82(16):1449–54.
Malek N, Baker MR, Mann C, Greene J. Electroencephalographic markers in dementia. Acta Neurol Scand. 2017;135(4):388–93.
Koenig T, Smailovic U, Jelic V. Past, present and future EEG in the clinical workup of dementias. Psychiatry Res Neuroimaging. 2020;306:111182.
Nardone R, Sebastianelli L, Versace V, Saltuari L, Lochner P, Frey V, et al. Usefulness of EEG techniques in distinguishing frontotemporal dementia from Alzheimer’s disease and other dementias. Dis Markers. 2018;2018:6581490.
Ieracitano C, Mammone N, Bramanti A, Hussain A, Morabito FC. A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing. 2019;323:96–107.
Smailovic U, Jelic V. Neurophysiological markers of Alzheimer’s disease: quantitative EEG approach. Neurol Ther. 2019;8(Suppl 2):37–55.
Peraza LR, Cromarty R, Kobeleva X, Firbank MJ, Killen A, Graziadio S, et al. Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients. Sci Rep. 2018;8(1):4637.
Aoki Y, Kazui H, Pascual-Marqui RD, Ishii R, Yoshiyama K, Kanemoto H, et al. EEG resting-state networks responsible for gait disturbance features in idiopathic normal pressure hydrocephalus. Clin EEG Neurosci. 2019;50(3):210–8.
Snaedal J, Johannesson GH, Gudmundsson TE, Blin NP, Emilsdottir AL, Einarsson B, et al. Diagnostic accuracy of statistical pattern recognition of electroencephalogram registration in evaluation of cognitive impairment and dementia. Dement Geriatr Cogn Disord. 2012;34(1):51–60.
Horst F, Lapuschkin S, Samek W, Muller KR, Schollhorn WI. Explaining the unique nature of individual gait patterns with deep learning. Sci Rep. 2019;9(1):2391.
McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, 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. Alzheimer’s Demen. 2011;7(3):263–9.
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM, et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of department of Health and human services task force on Alzheimer’s disease. Neurology. 1984;34(7):939–44.
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E, et al. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56(3):303–8.
McKeith IG, Ferman TJ, Thomas AJ, Blanc F, Boeve BF, Fujishiro H, et al. Research criteria for the diagnosis of prodromal dementia with Lewy bodies. Neurology. 2020;94(17):743–55.
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state.” A practical method for grading the cognitive state of patients for the clinician.A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98.
Farina FR, Emek-Savaş DD, Rueda-Delgado L, Boyle R, Kiiski H, Yener G, et al. A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment. Neuroimage. 2020;215:116795.
Aoe J, Fukuma R, Yanagisawa T, Harada T, Tanaka M, Kobayashi M, et al. Automatic diagnosis of neurological diseases using MEG signals with a deep neural network. Sci Rep. 2019;9(1):5057.
Hae H, Kang SJ, Kim WJ, Choi SY, Lee JG, Bae Y, et al. Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation. Plos Med. 2018;15(11):e1002693.
Zhou C, Wang Y, Ji MH, Tong J, Yang JJ, Xia H, et al. Predicting peritoneal metastasis of gastric cancer patients based on machine learning. Cancer Control. 2020;27(1):1073274820968900.
Cooney C, Korik A, Folli R, Coyle D. Evaluation of hyperparameter optimization in machine and deep learning methods for decoding imagined speech EEG. Sensors. 2020;20(16):4629.
Anderer P, Saletu B, Klöppel B, Semlitsch HV, Werner H. Discrimination be- tween demented patients and normals based on topographic eeg slow wave activity: comparison between z statistics, discriminant analysis and artificial neural network classifiers. Electroencephalogr Clin Neurophysiol. 1994;91(2):108–17.
Pritchard WS, Duke DW, Coburn KL, Moore NC, Tucker KA, Jann MW, et al. Eeg-based, neural-net predictive classification of Alzheimer’s disease versus control subjects is augmented by non-linear eeg measures. ElectroencephalogrClin Neurophysiol. 1994;91(2):118–30.
Huang C, Wahlund LO, Dierks T, Julin P, Winblad B, Jelic V, et al. Discrimination of Alzheimer’s disease and mild cognitive impairment by equivalent eeg sources: a cross-sectional and longitudinal study. Clin Neurophysiol. 2000;111(11):1961–7.
Trambaiolli LR, Lorena AC, Fraga FJ, Kanda PAM, Anghinah R, Nitrini R, et al. Improving Alzheimer’s disease diagnosis with machine learning techniques. Clin EEG Neurosci. 2011;42(3):160–5.
Morabito FC, Campolo M, Ieracitano C. Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer’s disease patients from scalp EEG recordings. Research and technologies for society and industry leveraging a better tomorrow (RTSI); 2016. IEEE 2nd international forum on(pp. 1–6).
Dauwan M, van der Zande JJ, van Dellen E, Sommer IEC, Scheltens P, Lemstra AW, et al. Random forest to differentiate dementia with Lewy bodies from Alzheimer’s disease. Alzheimers Dement. 2016;4:99–106.
Sand T, Bovim G, Gimse R. Quantitative electroencephalography in idiopathic normal pressure hydrocephalus: relationship to CSF outflow resistance and the CSF tap-test. Acta Neurol Scand. 1994;89(5):317–22.
Aoki Y, Kazui H, Tanaka T, Ishii R, Wada T, Ikeda S, et al. Noninvasive prediction of shunt operation outcome in idiopathic normal pressure hydrocephalus. Sci Rep. 2015;5:7775.
Mikolaenko I, Pletnikova O, Kawas CH, O’Brien R, Resnick SM, Crain B, et al. Alpha-synuclein lesions in normal aging, Parkinson disease, and Alzheimer disease: evidence from the Baltimore Longitudinal Study of Aging (BLSA). J Neuropathol Exp Neurol. 2005;64(2):156–62.
Hamilton R, Patel S, Lee EB, Jackson EM, Lopinto J, Arnold SE, et al. Lack of shunt response in suspected idiopathic normal pressure hydrocephalus with Alzheimer disease pathology. Ann Neurol. 2010;68(4):535–40.
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