{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T19:46:11Z","timestamp":1770234371929,"version":"3.49.0"},"reference-count":100,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T00:00:00Z","timestamp":1619222400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,4,24]]},"abstract":"<jats:p>Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer\u2019s disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer\u2019s disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>k<\/a:mi>\n                     <\/a:math>\n                  <\/jats:inline-formula>-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer\u2019s disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer\u2019s disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the <jats:inline-formula>\n                     <c:math xmlns:c=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                        <c:mi>k<\/c:mi>\n                     <\/c:math>\n                  <\/jats:inline-formula>-nearest neighbors\u2019 approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.<\/jats:p>","DOI":"10.1155\/2021\/5511922","type":"journal-article","created":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T17:21:37Z","timestamp":1619284897000},"page":"1-17","source":"Crossref","is-referenced-by-count":48,"title":["Diagnosis of Alzheimer\u2019s Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8033-9519","authenticated-orcid":true,"given":"Morteza","family":"Amini","sequence":"first","affiliation":[{"name":"Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0674-4428","authenticated-orcid":true,"given":"Mir Mohsen","family":"Pedram","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran"},{"name":"Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5351-6017","authenticated-orcid":true,"given":"AliReza","family":"Moradi","sequence":"additional","affiliation":[{"name":"Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran"},{"name":"Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5019-6828","authenticated-orcid":true,"given":"Mahshad","family":"Ouchani","sequence":"additional","affiliation":[{"name":"Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran"}]}],"member":"311","reference":[{"key":"1","volume-title":"Alzheimer's Disease International. Dementia: A Public Health Priority","author":"World Health Organization","year":"2012"},{"key":"2","first-page":"1","article-title":"Background paper 6.11: Alzheimer disease and other dementias","volume":"6","author":"B. Duthey","year":"2013","journal-title":"A Public Health Approach to Innovation"},{"key":"3","article-title":"The global impact of dementia: an analysis of prevalence, incidence, cost and trends","volume":"2015","author":"M. Prince","year":"2015","journal-title":"Alzheimer's Disease International"},{"issue":"2","key":"4","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.jagp.2017.06.015","article-title":"Complex clinical intersection: palliative care in patients with dementia","volume":"26","author":"E. E. Lee","year":"2018","journal-title":"The American Journal of Geriatric Psychiatry"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2011.03.004"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.34.7.939"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-1331.2010.03040.x"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/0022-3956(75)90026-6"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2008.04.014"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1111\/j.1532-5415.2005.53221.x"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1212\/01.wnl.0000434309.85312.19"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1007\/BF03324425"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1002\/1099-1166(200006)15:6<548::AID-GPS242>3.0.CO;2-U"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/906038"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/s00401-011-0910-3"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1177\/1550059413496779"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(14)70090-0"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2014.00047"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(16)00070-3"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2017.06.254"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaa13f"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1186\/1866-1955-5-24"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1016\/j.seizure.2015.01.012"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1097\/WNP.0000000000000281"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2011.02.011"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.3390\/e18010008"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2015.00025"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2016.00273"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2016.03.010"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1007\/s00702-017-1699-6"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195050387.001.0001"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9609.001.0001"},{"key":"33","volume-title":"Bioelectrical Signal Processing in Cardiac and Neurological Applications","author":"L. S\u00f6rnmo","year":"2005"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.4061\/2011\/539621"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2016.06.003"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1111\/ane.12638"},{"key":"37","doi-asserted-by":"publisher","DOI":"10.1177\/155005940503600303"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.1016\/S1388-2457(99)00013-9"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1016\/S0013-4694(97)00079-5"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1097\/00004691-200101000-00010"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnpbp.2012.05.001"},{"key":"42","doi-asserted-by":"publisher","DOI":"10.1159\/000106653"},{"key":"43","first-page":"1","article-title":"Stationary epoch-based entropy estimation for early diagnosis of Alzheimer's disease","author":"N. Houmani"},{"key":"44","doi-asserted-by":"publisher","DOI":"10.1142\/S012906571550032X"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/27\/3\/003"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.1109\/BSEC.2010.5510813"},{"key":"47","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2005.04.001"},{"key":"48","doi-asserted-by":"publisher","DOI":"10.1111\/j.1399-5618.2006.00375.x"},{"key":"49","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/27\/11\/004"},{"key":"50","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2005.07.004"},{"key":"51","doi-asserted-by":"publisher","DOI":"10.1109\/BIBE.2013.6701576"},{"key":"52","doi-asserted-by":"publisher","DOI":"10.3390\/s18103396"},{"key":"53","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.02.014"},{"key":"54","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.20598"},{"key":"55","doi-asserted-by":"publisher","DOI":"10.1007\/s40846-016-0214-0"},{"key":"56","first-page":"108","article-title":"A hybrid EEG signals classification approach based on grey wolf optimizer enhanced SVMs for epileptic detection","author":"A. Hamad"},{"key":"57","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3003-y"},{"key":"58","doi-asserted-by":"publisher","DOI":"10.1007\/s00221-016-4562-3"},{"key":"59","doi-asserted-by":"publisher","DOI":"10.1016\/j.yebeh.2018.02.010"},{"key":"60","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.09.017"},{"key":"61","volume-title":"Epileptic Seizure Detection: A Deep Learning Approach","author":"R. Hussein"},{"key":"62","volume-title":"EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approach for Feature Extraction and Classification Techniques","author":"N. Kulkarni","year":"2018"},{"key":"63","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2553131"},{"key":"64","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-014-0007-7"},{"key":"65","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"issue":"2","key":"66","doi-asserted-by":"crossref","first-page":"165","DOI":"10.23884\/ejt.2017.7.2.11","article-title":"An overview of popular deep learning methods","volume":"7","author":"M. Co\u015fkun","year":"2017","journal-title":"European Journal of Technic"},{"key":"67","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2014.09.007"},{"key":"68","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"69","doi-asserted-by":"publisher","DOI":"10.1177\/0037549717709932"},{"key":"70","doi-asserted-by":"publisher","DOI":"10.1109\/SIU.2018.8404385"},{"key":"71","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"72","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2018.07.004"},{"key":"73","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.03.016"},{"key":"74","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.09.009"},{"key":"75","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3689-5"},{"key":"76","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2919385"},{"key":"77","first-page":"112","article-title":"Predictive modeling of longitudinal data for Alzheimer's disease diagnosis using RNNs","author":"M. Aghili"},{"key":"78","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0190-7"},{"key":"79","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2011.2167628"},{"key":"80","doi-asserted-by":"publisher","DOI":"10.1166\/jmihi.2021.3259"},{"key":"81","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05666-0"},{"key":"82","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2020.107607"},{"key":"83","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108618"},{"key":"84","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2020.09.015"},{"key":"85","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2017.06.251"},{"key":"86","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2020.00255"},{"key":"87","doi-asserted-by":"publisher","DOI":"10.1080\/03772063.2016.1241164"},{"key":"88","doi-asserted-by":"publisher","DOI":"10.3233\/jad-200171"},{"key":"89","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(70)90143-4"},{"key":"90","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.03.010"},{"key":"91","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2005.1415167"},{"key":"92","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1977.10747"},{"key":"93","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2014.2330356"},{"key":"94","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"A. Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing System"},{"key":"95","article-title":"Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches","volume":"140, article 110170","author":"S. Hassantabar","year":"2020","journal-title":"Chaos, Solitons & Fractals"},{"key":"96","first-page":"565","article-title":"V-net: fully convolutional neural networks for volumetric medical image segmentation","author":"F. Milletari"},{"key":"97","doi-asserted-by":"publisher","DOI":"10.1109\/ICSMD50554.2020.9261662"},{"key":"98","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6210-3"},{"key":"99","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2004.5.1.11"},{"key":"100","first-page":"371","article-title":"The ten-twenty electrode system of the international federation","volume":"10","author":"H. H. Jasper","year":"1958","journal-title":"Electroencephalography and Clinical Neurophysiology"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/5511922.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/5511922.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/5511922.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T04:57:25Z","timestamp":1627016245000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2021\/5511922\/"}},"subtitle":[],"editor":[{"given":"Venkatesan","family":"Rajinikanth","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,4,24]]},"references-count":100,"alternative-id":["5511922","5511922"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5511922","relation":{},"ISSN":["1748-6718","1748-670X"],"issn-type":[{"value":"1748-6718","type":"electronic"},{"value":"1748-670X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,4,24]]}}}