{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T09:55:04Z","timestamp":1753869304085,"version":"3.41.2"},"reference-count":51,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,3,3]],"date-time":"2024-03-03T00:00:00Z","timestamp":1709424000000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071451","U21A20447"],"award-info":[{"award-number":["62071451","U21A20447"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Dementia\u2010associated disorders cause damage to the brains of patients and bring huge burdens to individuals and families. Electroencephalogram (EEG) monitoring is friendly to patients on account of low cost, non\u2010invasion, and objective. Event\u2010related potential (ERP) is a component of EEG that has huge potential to evaluate the cognitive function of the brain. In this study, we recorded the ERP from patients with dementia and healthy people, then proposed an ERP\u2010based deep learning method to realize dementia recognition via the model Dementia\u2010Unet (D\u2010Unet). To improve the performance of the model, on the base of the decoder and the primary classifier, we added new structures including a symmetric decoder and two auxiliary outputs. One of the auxiliary outputs was input reconstruction, and the other one was aimed at working like the primary classifier with the same task. The results of the experiment of four\u2010fold cross\u2010validation demonstrated the two auxiliary outputs improved the performance of the model effectively. When compared with some other machine learning methods and deep learning methods, our model obtained the best performance with an accuracy of 0.815, a precision of 0.829, a recall of 0.797, and an f1\u2010score of 0.812. Besides, we put up a complex training strategy with all outputs involved, but a simple testing strategy with only a primary classifier working to keep high performance but cut down the complexity burden during testing.<\/jats:p>","DOI":"10.1002\/ima.23047","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T04:20:33Z","timestamp":1709526033000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>D\u2010Unet<\/scp>: A symmetric architecture of convolutional neural network with two auxiliary outputs for dementia recognition"],"prefix":"10.1002","volume":"34","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0006-9774-7371","authenticated-orcid":false,"given":"Siying","family":"Li","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)  Beijing China"},{"name":"School of Electronic, Electrical and Communication Engineering University of Chinese Academy of Sciences  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Xia","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)  Beijing China"},{"name":"School of Electronic, Electrical and Communication Engineering University of Chinese Academy of Sciences  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonggang","family":"Zou","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)  Beijing China"},{"name":"School of Electronic, Electrical and Communication Engineering University of Chinese Academy of Sciences  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lidong","family":"Du","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfeng","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yundai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sixth Medical Center of Chinese PLA General Hospital  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yajun","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sixth Medical Center of Chinese PLA General Hospital  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Fang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS)  Beijing China"},{"name":"School of Electronic, Electrical and Communication Engineering University of Chinese Academy of Sciences  Beijing China"},{"name":"Personalized Management of Chronic Respiratory Disease Chinese Academy of Medical Sciences  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,3,3]]},"reference":[{"key":"e_1_2_12_2_1","first-page":"84","article-title":"The global impact of dementia: an analysis of prevalence, incidence, cost and trends","volume":"2015","author":"Prince M","year":"2015","journal-title":"World Alzheimer Report"},{"key":"e_1_2_12_3_1","unstructured":"AnzarootS McCallumA.UMass Citation Field Extraction Dataset.2013https:\/\/2.zoppoz.workers.dev:443\/http\/www.iesl.cs.umass.edu\/data\/data-umasscitationfield"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.07.006"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.07.001"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.11622\/smedj.2022085"},{"key":"e_1_2_12_7_1","doi-asserted-by":"crossref","unstructured":"XuJ JiaZ WangW et al.A novel neural network for P300 brain\u2010computer Interface signal recognition. 2021 IEEE SmartWorld Ubiquitous Intelligence & Computing Advanced & Trusted Computing Scalable Computing & Communications Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/IOP\/SCI). IEEE 2021. doi:10.1109\/SWC50871.2021.00071","DOI":"10.1109\/SWC50871.2021.00071"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2007.10.008"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.3233\/JAD\u2010160056"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.04.021"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2019.2929053"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521\u2010018\u20103689\u20105"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2019.2937768"},{"key":"e_1_2_12_14_1","doi-asserted-by":"crossref","unstructured":"SidulovaM NehmeN ParkCH.Towards explainable image analysis for Alzheimer's disease and mild cognitive impairment diagnosis. 2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE 2021. doi:10.1109\/AIPR52630.2021.9762082","DOI":"10.1109\/AIPR52630.2021.9762082"},{"key":"e_1_2_12_15_1","doi-asserted-by":"crossref","unstructured":"PuriD NalbalwarS NandgaonkarA et al.Alzheimer's disease detection using empirical mode decomposition and Hjorth parameters of EEG signal. 2022 International Conference on Decision Aid Sciences and Applications (DASA). IEEE 2022. doi:10.1109\/DASA54658.2022.9765111","DOI":"10.1109\/DASA54658.2022.9765111"},{"key":"e_1_2_12_16_1","doi-asserted-by":"crossref","unstructured":"LiY XiaoS LiY LiY YangB.Classification of Mild Cognitive Impairment from multi\u2010domain features of resting\u2010state EEG. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE 2020. doi:10.1109\/EMBC44109.2020.9176053","DOI":"10.1109\/EMBC44109.2020.9176053"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13195\u2010022\u201001046\u2010z"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704\u2010015\u20104478\u20102"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12559\u2010022\u201010033\u20103"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3025855"},{"key":"e_1_2_12_21_1","doi-asserted-by":"crossref","unstructured":"GkeniosG LatsiouK DiamantarasK et al.Diagnosis of Alzheimer's disease and mild cognitive impairment using EEG and recurrent neural networks. 2022 44th annual international conference of the IEEE engineering in Medicine & Biology Society (EMBC). IEEE 2022. doi:10.1109\/EMBC48229.2022.9871302","DOI":"10.1109\/EMBC48229.2022.9871302"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3204913"},{"key":"e_1_2_12_23_1","unstructured":"OdenaA OlahC ShlensJ.Conditional image synthesis with auxiliary classifier gans. International Conference on Machine Learning. PMLR 2017.https:\/\/2.zoppoz.workers.dev:443\/https\/proceedings.mlr.press\/v70\/odena17a.html"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3191682"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx105"},{"key":"e_1_2_12_26_1","doi-asserted-by":"crossref","unstructured":"SzegedyC LiuW JiaY et al.Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition2015. doi:10.1109\/cvpr.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911\u2010018\u20100613\u2010y"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.3233\/JAD\u2010160188"},{"key":"e_1_2_12_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106841"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci9040081"},{"key":"e_1_2_12_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11081437"},{"key":"e_1_2_12_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10044\u2010020\u201000910\u20108"},{"key":"e_1_2_12_33_1","doi-asserted-by":"crossref","unstructured":"Al\u2010NuaimiAH JammehE SunL IfeachorE.Higuchi fractal dimension of the electroencephalogram as a biomarker for early detection of Alzheimer's disease. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE 2017. doi:10.1109\/EMBC.2017.8037320","DOI":"10.1109\/EMBC.2017.8037320"},{"key":"e_1_2_12_34_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00641"},{"key":"e_1_2_12_35_1","doi-asserted-by":"crossref","unstructured":"AdebisiAT GonuguntlaV LeeH\u2010W et al.Classification of dementia associated disorders using eeg based frequent subgraph technique. 2020 International Conference on Data Mining Workshops (ICDMW). IEEE 2020. doi:10.1109\/ICDMW51313.2020.00087","DOI":"10.1109\/ICDMW51313.2020.00087"},{"key":"e_1_2_12_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/app12115413"},{"key":"e_1_2_12_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102223"},{"key":"e_1_2_12_38_1","doi-asserted-by":"crossref","unstructured":"T\u0103u\u0163anA\u2010M CasulaE BorghiI et al.Preliminary study on the impact of EEG density on TMS\u2010EEG classification in Alzheimer's disease. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE 2022. doi:10.1109\/EMBC48229.2022.9870920","DOI":"10.1109\/EMBC48229.2022.9870920"},{"key":"e_1_2_12_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102338"},{"key":"e_1_2_12_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.09.084"},{"key":"e_1_2_12_41_1","doi-asserted-by":"crossref","unstructured":"IeracitanoC MammoneN BramantiA et al.A time\u2010frequency based machine learning system for brain states classification via eeg signal processing. 2019 International Joint Conference on Neural Networks (IJCNN). IEEE 2019. doi:10.1109\/IJCNN.2019.8852240","DOI":"10.1109\/IJCNN.2019.8852240"},{"key":"e_1_2_12_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103049"},{"key":"e_1_2_12_43_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering9040141"},{"key":"e_1_2_12_44_1","doi-asserted-by":"crossref","unstructured":"IeracitanoC MammoneN HussainA MorabitoFC et al.A Convolutional Neural Network based self\u2010learning approach for classifying neurodegenerative states from EEG signals in dementia. 2020 International Joint Conference on Neural Networks (IJCNN). IEEE 2020. doi:10.1109\/IJCNN48605.2020.9207167","DOI":"10.1109\/IJCNN48605.2020.9207167"},{"key":"e_1_2_12_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3198988"},{"key":"e_1_2_12_46_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741\u20102552\/aace8c"},{"key":"e_1_2_12_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2021.3070327"},{"key":"e_1_2_12_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2018.2817622"},{"key":"e_1_2_12_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978\u20103\u2010540\u201036668\u20103_30"},{"key":"e_1_2_12_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115\u2010017\u20101059\u20108"},{"key":"e_1_2_12_51_1","doi-asserted-by":"crossref","unstructured":"HeK ZhangX RenS et al.Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. doi:10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_12_52_1","doi-asserted-by":"crossref","unstructured":"HuangG LiuZ van derMaatenL WeinbergerKQ et al.Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. doi:10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["International Journal of Imaging Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ima.23047","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T06:30:22Z","timestamp":1711693822000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/10.1002\/ima.23047"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1002\/ima.23047"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/ima.23047","archive":["Portico"],"relation":{},"ISSN":["0899-9457","1098-1098"],"issn-type":[{"type":"print","value":"0899-9457"},{"type":"electronic","value":"1098-1098"}],"subject":[],"published":{"date-parts":[[2024,3]]},"assertion":[{"value":"2023-11-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e23047"}}