{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:43:40Z","timestamp":1777985020355,"version":"3.51.4"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"EPSRC","doi-asserted-by":"publisher","award":["EP\/M026981\/1"],"award-info":[{"award-number":["EP\/M026981\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1016\/j.neucom.2018.09.071","type":"journal-article","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T23:10:02Z","timestamp":1538781002000},"page":"96-107","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":261,"special_numbering":"C","title":["A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings"],"prefix":"10.1016","volume":"323","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7890-2897","authenticated-orcid":false,"given":"Cosimo","family":"Ieracitano","sequence":"first","affiliation":[]},{"given":"Nadia","family":"Mammone","sequence":"additional","affiliation":[]},{"given":"Alessia","family":"Bramanti","sequence":"additional","affiliation":[]},{"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[]},{"given":"Francesco C.","family":"Morabito","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"9518","key":"10.1016\/j.neucom.2018.09.071_bib0001","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/S0140-6736(06)68542-5","article-title":"Mild cognitive impairment","volume":"367","author":"Gauthier","year":"2006","journal-title":"Lancet"},{"issue":"7","key":"10.1016\/j.neucom.2018.09.071_bib0002","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.clinph.2004.01.001","article-title":"Eeg dynamics in patients with alzheimer\u2019s disease","volume":"115","author":"Jeong","year":"2004","journal-title":"Clin. Neurophysiol."},{"issue":"3","key":"10.1016\/j.neucom.2018.09.071_bib0003","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.clinph.2004.09.022","article-title":"Disturbed fluctuations of resting state eeg synchronization in alzheimer\u2019s disease","volume":"116","author":"Stam","year":"2005","journal-title":"Clin. Neurophysiol."},{"key":"10.1016\/j.neucom.2018.09.071_bib0004","unstructured":"J.S. misc, A.G. Piersol, Engineering Applications of Correlation and Spectral Analysis, New York, Wiley-Interscience, vol. 315, (1980)."},{"issue":"9","key":"10.1016\/j.neucom.2018.09.071_bib0005","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1016\/j.clinph.2007.05.070","article-title":"Eeg correlates in the spectrum of cognitive decline","volume":"118","author":"Van der Hiele","year":"2007","journal-title":"Clin. Neurophysiol."},{"issue":"2","key":"10.1016\/j.neucom.2018.09.071_bib0006","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12559-016-9396-6","article-title":"A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay","volume":"8","author":"Zeng","year":"2016","journal-title":"Cognit. Comput."},{"issue":"4","key":"10.1016\/j.neucom.2018.09.071_bib0007","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1007\/s12559-016-9404-x","article-title":"Deep belief networks for quantitative analysis of a gold immunochromatographic strip","volume":"8","author":"Zeng","year":"2016","journal-title":"Cognit. Comput."},{"issue":"6","key":"10.1016\/j.neucom.2018.09.071_bib0008","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1109\/TNNLS.2018.2790388","article-title":"Applications of deep learning and reinforcement learning to biological data","volume":"29","author":"Mahmud","year":"2018","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst."},{"issue":"2","key":"10.1016\/j.neucom.2018.09.071_bib0009","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3390\/e20020043","article-title":"Information theoretic-based interpretation of a deep neural network approach in diagnosing psychogenic non-epileptic seizures","volume":"20","author":"Gasparini","year":"2018","journal-title":"Entropy"},{"key":"10.1016\/j.neucom.2018.09.071_bib0010","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.neucom.2017.08.043","article-title":"Facial expression recognition via learning deep sparse autoencoders","volume":"273","author":"Zeng","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2018.09.071_bib0011","series-title":"9th International Conference, BICS 2018, Xi'an, China, July 7-8, 2018, Proceedings","article-title":"Statistical analysis driven optimized deep learning system for intrusion detection","author":"Ieracitano","year":"2018"},{"key":"10.1016\/j.neucom.2018.09.071_bib0012","series-title":"Proceedings of the Advances in Neural Information Processing Systems","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"key":"10.1016\/j.neucom.2018.09.071_bib0013","series-title":"Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"3517","article-title":"On rectified linear units for speech processing","author":"Zeiler","year":"2013"},{"key":"10.1016\/j.neucom.2018.09.071_bib0014","series-title":"Proceedings of the 27th International Conference on Machine Learning (ICML-10)","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"Nair","year":"2010"},{"issue":"2","key":"10.1016\/j.neucom.2018.09.071_bib0015","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/0013-4694(94)90032-9","article-title":"Discrimination between demented patients and normals based on topographic eeg slow wave activity: comparison between z statistics, discriminant analysis and artificial neural network classifiers","volume":"91","author":"Anderer","year":"1994","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"issue":"2","key":"10.1016\/j.neucom.2018.09.071_bib0016","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/0013-4694(94)90033-7","article-title":"Eeg-based, neural-net predictive classification of Alzheimer\u2019s disease versus control subjects is augmented by non-linear eeg measures","volume":"91","author":"Pritchard","year":"1994","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"issue":"3","key":"10.1016\/j.neucom.2018.09.071_bib0017","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1177\/155005941104200304","article-title":"Improving Alzheimer\u2019s disease diagnosis with machine learning techniques","volume":"42","author":"Trambaiolli","year":"2011","journal-title":"Clin. EEG Neurosci."},{"issue":"11","key":"10.1016\/j.neucom.2018.09.071_bib0018","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1016\/S1388-2457(00)00454-5","article-title":"Discrimination of Alzheimer\u2019s disease and mild cognitive impairment by equivalent eeg sources: a cross-sectional and longitudinal study","volume":"111","author":"Huang","year":"2000","journal-title":"Clin. Neurophysiol."},{"issue":"2","key":"10.1016\/j.neucom.2018.09.071_bib0019","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.artmed.2007.02.006","article-title":"The ifast model, a novel parallel nonlinear eeg analysis technique, distinguishes mild cognitive impairment and Alzheimer\u2019s disease patients with high degree of accuracy","volume":"40","author":"Buscema","year":"2007","journal-title":"Artif. Intell. Med."},{"issue":"7","key":"10.1016\/j.neucom.2018.09.071_bib0020","doi-asserted-by":"crossref","first-page":"1534","DOI":"10.1016\/j.clinph.2008.03.026","article-title":"Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?","volume":"119","author":"Rossini","year":"2008","journal-title":"Clin. Neurophysiol."},{"issue":"2","key":"10.1016\/j.neucom.2018.09.071_bib0021","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.cmpb.2014.01.019","article-title":"Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer\u2019s disease","volume":"114","author":"McBride","year":"2014","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"7553","key":"10.1016\/j.neucom.2018.09.071_bib0022","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.neucom.2018.09.071_bib0023","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/72.554195","article-title":"Face recognition: a convolutional neural-network approach","volume":"8","author":"Lawrence","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.neucom.2018.09.071_bib0024","unstructured":"A. Payan, G. Montana, Predicting Alzheimer\u2019s disease: a neuroimaging study with 3d convolutional neural networks, arXiv: 1502.02506 (2015)."},{"key":"10.1016\/j.neucom.2018.09.071_bib0025","series-title":"Proceedings of the IEEE International Conference on Image Processing (ICIP)","first-page":"126","article-title":"Alzheimer\u2019s disease diagnostics by adaptation of 3d convolutional network","author":"Hosseini-Asl","year":"2016"},{"key":"10.1016\/j.neucom.2018.09.071_bib0026","series-title":"Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing","first-page":"34","article-title":"Fuseme: classification of SMRI images by fusion of deep CNNs in 2d+ projections","author":"Aderghal","year":"2017"},{"key":"10.1016\/j.neucom.2018.09.071_bib0027","unstructured":"S. Sarraf, G. Tofighi, Classification of Alzheimer\u2019s disease using fMRI data and deep learning convolutional neural networks, arXiv: 1603.08631 (2016)."},{"key":"10.1016\/j.neucom.2018.09.071_bib0028","series-title":"Proceedings of the IEEE Region 10 Conference (TENCON)","first-page":"3724","article-title":"DemNet: a convolutional neural network for the detection of Alzheimer\u2019s disease and mild cognitive impairment","author":"Billones","year":"2016"},{"key":"10.1016\/j.neucom.2018.09.071_bib0029","series-title":"Proceedings of the IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI)","first-page":"1","article-title":"Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer\u2019s disease patients from scalp eeg recordings","author":"Morabito","year":"2016"},{"issue":"02","key":"10.1016\/j.neucom.2018.09.071_bib0030","doi-asserted-by":"crossref","first-page":"1650039","DOI":"10.1142\/S0129065716500398","article-title":"Deep learning representation from electroencephalography of early-stage Creutzfeldt\u2013Jakob disease and features for differentiation from rapidly progressive dementia","volume":"27","author":"Morabito","year":"2017","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.neucom.2018.09.071_bib0031","series-title":"Diagnostic and Statistical Manual of Mental Disorders","year":"2013"},{"issue":"1","key":"10.1016\/j.neucom.2018.09.071_bib0032","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.neucom.2018.09.071_bib0033","unstructured":"G. Heinzel, A. R\u00fcdiger, R. Schilling, Spectrum and spectral density estimation by the discrete fourier transform (DFT), including a comprehensive list of window functions and some new at-top windows (2002)."},{"key":"10.1016\/j.neucom.2018.09.071_bib0034","series-title":"Digital Signal Processing: Principles Algorithms and Applications","author":"Proakis","year":"2001"},{"key":"10.1016\/j.neucom.2018.09.071_bib0035","series-title":"Proceedings of the Artificial Neural Networks\u2013ICANN 2010","first-page":"92","article-title":"Evaluation of pooling operations in convolutional architectures for object recognition","author":"Scherer","year":"2010"},{"key":"10.1016\/j.neucom.2018.09.071_bib0036","unstructured":"P. Goyal, P. Doll\u00e1r, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, K. He, Accurate, large minibatch SGD: Training Imagenet in 1 hour, arXiv: 1706.02677 (2017)."},{"key":"10.1016\/j.neucom.2018.09.071_bib0037","series-title":"Neural networks: Tricks of the trade","first-page":"437","article-title":"Practical recommendations for gradient-based training of deep architectures","author":"Bengio","year":"2012"},{"key":"10.1016\/j.neucom.2018.09.071_bib0038","series-title":"Proceedings of the International Conference on Machine Learning","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"key":"10.1016\/j.neucom.2018.09.071_bib0039","unstructured":"D.M. Powers, Evaluation: from precision, recall and f-measure to ROC, informedness, markedness and correlation (2011)."},{"issue":"1","key":"10.1016\/j.neucom.2018.09.071_bib0040","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13195-015-0162-x","article-title":"Conversion of mild cognitive impairment patients in Alzheimers disease: prognostic value of alpha3\/alpha2 electroencephalographic rhythms power ratio","volume":"7","author":"Moretti","year":"2015","journal-title":"Alzh. Res. Ther."},{"issue":"3","key":"10.1016\/j.neucom.2018.09.071_bib0041","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0987-7053(01)00254-4","article-title":"Diagnostic value of quantitative EEG in Alzheimers disease","volume":"31","author":"Bennys","year":"2001","journal-title":"Neurophysiol. Clin. Clin. Neurophysiol."},{"issue":"2","key":"10.1016\/j.neucom.2018.09.071_bib0042","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/S1388-2457(03)00345-6","article-title":"Individual analysis of EEG frequency and band power in mild Alzheimer\u2019s disease","volume":"115","author":"Moretti","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"10.1016\/j.neucom.2018.09.071_bib0043","article-title":"Three-dimensional local energy-based shape histogram (3d-lesh)-based feature extraction\u2013a novel technique","author":"Wajid","year":"2017","journal-title":"Expert Syst. Appl."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231218311524?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231218311524?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T11:26:21Z","timestamp":1541071581000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231218311524"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":43,"alternative-id":["S0925231218311524"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2018.09.071","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2019,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2018.09.071","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Crown Copyright \u00a9 2018 Published by Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}