{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T22:42:18Z","timestamp":1790894538952,"version":"4.1.0"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin Municipality","doi-asserted-by":"publisher","award":["22JCYBJC00470"],"award-info":[{"award-number":["22JCYBJC00470"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianjin Science and Popularization of Science and Research Development Project","award":["22KPXMRC00170"],"award-info":[{"award-number":["22KPXMRC00170"]}]},{"name":"Tianjin Municipal Education Commission Scientific Research Program","award":["2022KJ106"],"award-info":[{"award-number":["2022KJ106"]}]},{"name":"Unveiling and Commanding Project of Tianjin University of Technology and Education","award":["XJJB2104"],"award-info":[{"award-number":["XJJB2104"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Neurodyn"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s11571-024-10130-z","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T07:02:06Z","timestamp":1718002926000},"page":"2993-3013","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automatic detection of Alzheimer\u2019s disease from EEG signals using an improved AFS\u2013GA hybrid algorithm"],"prefix":"10.1007","volume":"18","author":[{"given":"Ruofan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiguang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianshuan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5988-7908","authenticated-orcid":false,"given":"Yanqiu","family":"Che","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojie","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changzhi","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"key":"10130_CR1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.knosys.2015.08.004","volume":"88","author":"UR Acharya","year":"2015","unstructured":"Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE (2015) Application of entropies for automated diagnosis of epilepsy using eeg signals: A review. Knowl-Based Syst 88:85\u201396","journal-title":"Knowl-Based Syst"},{"issue":"11","key":"10130_CR2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.5815\/ijigsp.2019.11.04","volume":"11","author":"H Akbari","year":"2019","unstructured":"Akbari H, Ghofrani S, Ghofrani S (2019) Fast and accurate classification f and nf eeg by using sodp and ewt. Int J Image Graph Signal Process 11(11):29\u201335","journal-title":"Int J Image Graph Signal Process"},{"key":"10130_CR3","doi-asserted-by":"crossref","unstructured":"Akbari H, Sadiq MT, Payan M, Esmaili SS, Baghri H, Bagheri H (2021) Depression detection based on geometrical features extracted from sodp shape of eeg signals and binary pso. Traitement du Signal 38(1)","DOI":"10.18280\/ts.380102"},{"key":"10130_CR4","doi-asserted-by":"crossref","unstructured":"Akbari H, Sadiq MT, Payan M, Esmaili SS, Baghri H, Bagheri H (2021) Depression detection based on geometrical features extracted from sodp shape of eeg signals and binary pso. Traitement du Signal 38(1)","DOI":"10.18280\/ts.380102"},{"key":"10130_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13634-015-0251-9","volume":"2015","author":"T Alotaiby","year":"2015","unstructured":"Alotaiby T, El-Samie FEA, Alshebeili SA, Ahmad I (2015) A review of channel selection algorithms for eeg signal processing. EURASIP J Adv Signal Process 2015:1\u201321","journal-title":"EURASIP J Adv Signal Process"},{"key":"10130_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13634-015-0251-9","volume":"2015","author":"T Alotaiby","year":"2015","unstructured":"Alotaiby T, El-Samie FEA, Alshebeili SA, Ahmad I (2015) A review of channel selection algorithms for eeg signal processing. EURASIP J Adv Signal Process 2015:1\u201321","journal-title":"EURASIP J Adv Signal Process"},{"key":"10130_CR7","doi-asserted-by":"crossref","unstructured":"Al-Sharhan S, Karray F, Gueaieb W, Basir O (2001) Fuzzy entropy: a brief survey. In: 10th IEEE international conference on fuzzy systems.(Cat. No. 01CH37297), vol 3. IEEE, pp 1135\u20131139","DOI":"10.1109\/FUZZ.2001.1008855"},{"key":"10130_CR8","volume":"15","author":"M Ando","year":"2021","unstructured":"Ando M, Nobukawa S, Kikuchi M, Takahashi T (2021) Identification of electroencephalogram signals in alzheimer\u2019s disease by multifractal and multiscale entropy analysis. Front Neurosci 15:667614","journal-title":"Front Neurosci"},{"key":"10130_CR9","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.neulet.2018.11.034","volume":"694","author":"S Ayd\u0131n","year":"2019","unstructured":"Ayd\u0131n S, G\u00fcd\u00fcc\u00fc \u00c7, Kutluk F, \u00d6niz A, \u00d6zg\u00f6ren M (2019) The impact of musical experience on neural sound encoding performance. Neurosci Lett 694:124\u2013128","journal-title":"Neurosci Lett"},{"key":"10130_CR10","doi-asserted-by":"crossref","unstructured":"Azami H, Daftarifard E, Humeau-Heurtier A, Fernandez A, Abasolo D, Rajji TK (2023) Assessment and comparison of nonlinear measures in resting-state magnetoencephalograms in alzheimer\u2019s disease and mild cognitive impairment. J Alzheimer\u2019s Dis (Preprint), 1\u201312","DOI":"10.3233\/JAD-230544"},{"key":"10130_CR11","volume":"77","author":"R Bai","year":"2022","unstructured":"Bai R, Guo J, Ye X-Y, Xie Y, Xie T (2022) Oxidative stress: the core pathogenesis and mechanism of alzheimer\u2019s disease. Ageing Res Rev 77:101619","journal-title":"Ageing Res Rev"},{"key":"10130_CR12","doi-asserted-by":"crossref","first-page":"99670","DOI":"10.1109\/ACCESS.2019.2927267","volume":"7","author":"S Bavkar","year":"2019","unstructured":"Bavkar S, Iyer B, Deosarkar S (2019) Rapid screening of alcoholism: an eeg based optimal channel selection approach. IEEE Access 7:99670\u201399682","journal-title":"IEEE Access"},{"issue":"3","key":"10130_CR13","first-page":"527","volume":"11","author":"S Borde","year":"2023","unstructured":"Borde S, Ratnaparkhe V (2023) Optimization in channel selection for eeg signal analysis of sleep disorder subjects. J Integr Sci Technol 11(3):527\u2013527","journal-title":"J Integr Sci Technol"},{"key":"10130_CR14","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.neucom.2018.07.019","volume":"314","author":"L Cai","year":"2018","unstructured":"Cai L, Wei X, Wang J, Yu H, Deng B, Wang R (2018) Reconstruction of functional brain network in alzheimer\u2019s disease via cross-frequency phase synchronization. Neurocomputing 314:490\u2013500","journal-title":"Neurocomputing"},{"key":"10130_CR15","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3389\/fnagi.2014.00055","volume":"6","author":"R Cassani","year":"2014","unstructured":"Cassani R, Falk TH, Fraga FJ, Kanda PA, Anghinah R (2014) The effects of automated artifact removal algorithms on electroencephalography-based alzheimer\u2019s disease diagnosis. Front Aging Neurosci 6:55","journal-title":"Front Aging Neurosci"},{"key":"10130_CR16","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.114040","volume":"225","author":"A Cataldo","year":"2024","unstructured":"Cataldo A, Criscuolo S, De Benedetto E, Masciullo A, Pesola M, Picone J, Schiavoni R (2024) Eeg complexity-based algorithm using multiscale fuzzy entropy: towards a detection of alzheimer\u2019s disease. Measurement 225:114040","journal-title":"Measurement"},{"issue":"5","key":"10130_CR17","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1177\/15500594211064008","volume":"53","author":"FH \u00c7etin","year":"2022","unstructured":"\u00c7etin FH, Bar\u0131\u015f Usta M, Ayd\u0131n S, G\u00fcven AS (2022) A case study on eeg analysis: embedding entropy estimations indicate the decreased neuro-cortical complexity levels mediated by methylphenidate treatment in children with adhd. Clin EEG Neurosci 53(5):406\u2013417","journal-title":"Clin EEG Neurosci"},{"issue":"1","key":"10130_CR18","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.medengphy.2008.04.005","volume":"31","author":"W Chen","year":"2009","unstructured":"Chen W, Zhuang J, Yu W, Wang Z (2009) Measuring complexity using fuzzyen, apen, and sampen. Med Eng Phys 31(1):61\u201368","journal-title":"Med Eng Phys"},{"key":"10130_CR19","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2019.101760","volume":"57","author":"M Demuru","year":"2020","unstructured":"Demuru M, La Cava SM, Pani SM, Fraschini M (2020) A comparison between power spectral density and network metrics: an eeg study. Biomed Signal Process Control 57:101760","journal-title":"Biomed Signal Process Control"},{"key":"10130_CR20","volume":"13","author":"DNT Doan","year":"2021","unstructured":"Doan DNT, Ku B, Choi J, Oh M, Kim K, Cha W, Kim JU (2021) Predicting dementia with prefrontal electroencephalography and event-related potential. Front Aging Neurosci 13:659817","journal-title":"Front Aging Neurosci"},{"issue":"1","key":"10130_CR21","doi-asserted-by":"crossref","first-page":"116","DOI":"10.3390\/e22010116","volume":"22","author":"I Echegoyen","year":"2020","unstructured":"Echegoyen I, L\u00f3pez-Sanz D, Mart\u00ednez JH, Maest\u00fa F, Buld\u00fa JM (2020) Permutation entropy and statistical complexity in mild cognitive impairment and alzheimer\u2019s disease: an analysis based on frequency bands. Entropy 22(1):116","journal-title":"Entropy"},{"issue":"1","key":"10130_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40708-021-00123-7","volume":"8","author":"AA Ein Shoka","year":"2021","unstructured":"Ein Shoka AA, Alkinani MH, El-Sherbeny A, El-Sayed A, Dessouky MM (2021) Automated seizure diagnosis system based on feature extraction and channel selection using eeg signals. Brain Inform 8(1):1\u201316","journal-title":"Brain Inform"},{"key":"10130_CR23","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s11571-020-09626-1","volume":"15","author":"Z Gao","year":"2021","unstructured":"Gao Z, Dang W, Wang X, Hong X, Hou L, Ma K, Perc M (2021) Complex networks and deep learning for eeg signal analysis. Cogn Neurodyn 15:369\u2013388","journal-title":"Cogn Neurodyn"},{"issue":"1","key":"10130_CR24","first-page":"1598","volume":"29","author":"M Ghosh","year":"2019","unstructured":"Ghosh M, Guha R, Alam I, Lohariwal P, Jalan D, Sarkar R (2019) Binary genetic swarm optimization: a combination of ga and pso for feature selection. J Intell Syst 29(1):1598\u20131610","journal-title":"J Intell Syst"},{"key":"10130_CR25","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.bbr.2019.01.018","volume":"362","author":"H Hadoush","year":"2019","unstructured":"Hadoush H, Alafeef M, Abdulhay E (2019) Automated identification for autism severity level: Eeg analysis using empirical mode decomposition and second order difference plot. Behav Brain Res 362:240\u2013248","journal-title":"Behav Brain Res"},{"key":"10130_CR26","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.bbr.2019.01.018","volume":"362","author":"H Hadoush","year":"2019","unstructured":"Hadoush H, Alafeef M, Abdulhay E (2019) Automated identification for autism severity level: Eeg analysis using empirical mode decomposition and second order difference plot. Behav Brain Res 362:240\u2013248","journal-title":"Behav Brain Res"},{"issue":"1","key":"10130_CR27","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Genetic algorithms. Sci Am 267(1):66\u201373","journal-title":"Sci Am"},{"issue":"4","key":"10130_CR28","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.3390\/app10041244","volume":"10","author":"CF Hsu","year":"2020","unstructured":"Hsu CF, Chao H-H, Yang AC, Yeh C-W, Hsu L, Chi S (2020) Discrimination of severity of alzheimer\u2019s disease with multiscale entropy analysis of eeg dynamics. Appl Sci 10(4):1244","journal-title":"Appl Sci"},{"key":"10130_CR29","unstructured":"Jellinger KA (2020) Neuropathology of the alzheimer\u2019s continuum: an update. Free Neuropathol 1"},{"issue":"1","key":"10130_CR30","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.3233\/BME-130919","volume":"24","author":"X Jie","year":"2014","unstructured":"Jie X, Cao R, Li L (2014) Emotion recognition based on the sample entropy of eeg. Bio-Med Mater Eng 24(1):1185\u20131192","journal-title":"Bio-Med Mater Eng"},{"key":"10130_CR31","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.122964","volume":"242","author":"S Karasu","year":"2022","unstructured":"Karasu S, Altan A (2022) Crude oil time series prediction model based on lstm network with chaotic henry gas solubility optimization. Energy 242:122964","journal-title":"Energy"},{"key":"10130_CR32","doi-asserted-by":"crossref","unstructured":"Kira K, Rendell LA (1992) A practical approach to feature selection. In: Machine learning proceedings 1992. Elsevier, pp 249\u2013256","DOI":"10.1016\/B978-1-55860-247-2.50037-1"},{"key":"10130_CR33","doi-asserted-by":"crossref","unstructured":"Lambora A, Gupta K, Chopra K (2019) Genetic algorithm-A literature review. In: 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon). IEEE","DOI":"10.1109\/COMITCon.2019.8862255"},{"issue":"11","key":"10130_CR34","first-page":"32","volume":"22","author":"X-l Li","year":"2002","unstructured":"Li X-l (2002) An optimizing method based on autonomous animats: fish-swarm algorithm. Syst Eng Theory Pract 22(11):32\u201338","journal-title":"Syst Eng Theory Pract"},{"issue":"1","key":"10130_CR35","doi-asserted-by":"crossref","first-page":"92","DOI":"10.3390\/app7010092","volume":"7","author":"M Li","year":"2017","unstructured":"Li M, Liu H, Zhu W, Yang J (2017) Applying improved multiscale fuzzy entropy for feature extraction of mi-eeg. Appl Sci 7(1):92","journal-title":"Appl Sci"},{"issue":"3","key":"10130_CR36","first-page":"0193691","volume":"13","author":"P Li","year":"2018","unstructured":"Li P, Karmakar C, Yearwood J, Venkatesh S, Palaniswami M, Liu C (2018) Detection of epileptic seizure based on entropy analysis of short-term eeg. PLoS ONE 13(3):0193691","journal-title":"PLoS ONE"},{"key":"10130_CR37","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1007\/s11064-020-03215-8","volume":"46","author":"J Li","year":"2021","unstructured":"Li J, Xu C, Zhang J, Jin C, Shi X, Zhang C, Jia S, Xu J, Gui X, Xing L et al (2021) Identification of mirna-target gene pairs in the parietal and frontal lobes of the brain in patients with alzheimer\u2019s disease using bioinformatic analyses. Neurochem Res 46:964\u2013979","journal-title":"Neurochem Res"},{"issue":"4","key":"10130_CR38","volume":"18","author":"F Li","year":"2021","unstructured":"Li F, Jiang L, Liao Y, Si Y, Yi C, Zhang Y, Zhu X, Yang Z, Yao D, Cao Z et al (2021) Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp eeg study. J Neural Eng 18(4):046097","journal-title":"J Neural Eng"},{"issue":"4","key":"10130_CR39","doi-asserted-by":"crossref","first-page":"457","DOI":"10.3390\/e23040457","volume":"23","author":"Q Liu","year":"2021","unstructured":"Liu Q, Liu Y, Chen K, Wang L, Li Z, Ai Q, Ma L (2021) Research on channel selection and multi-feature fusion of eeg signals for mental fatigue detection. Entropy 23(4):457","journal-title":"Entropy"},{"key":"10130_CR40","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.neucom.2015.06.090","volume":"174","author":"X-Y Luan","year":"2016","unstructured":"Luan X-Y, Li Z-P, Liu T-Z (2016) A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm. Neurocomputing 174:522\u2013529","journal-title":"Neurocomputing"},{"issue":"19","key":"10130_CR41","doi-asserted-by":"crossref","first-page":"10889","DOI":"10.3390\/ijms221910889","volume":"22","author":"P Monllor","year":"2021","unstructured":"Monllor P, Cervera-Ferri A, Lloret M-A, Esteve D, Lopez B, Leon J-L, Lloret A (2021) Electroencephalography as a non-invasive biomarker of alzheimer\u2019s disease: a forgotten candidate to substitute csf molecules? Int J Mol Sci 22(19):10889","journal-title":"Int J Mol Sci"},{"issue":"37","key":"10130_CR42","doi-asserted-by":"crossref","first-page":"2204179119","DOI":"10.1073\/pnas.2204179119","volume":"119","author":"Z-K Ngian","year":"2022","unstructured":"Ngian Z-K, Tan Y-Y, Choo C-T, Lin W-Q, Leow C-Y, Mah S-J, Lai MK-P, Chen CL-H, Ong C-T (2022) Truncated tau caused by intron retention is enriched in alzheimer\u2019s disease cortex and exhibits altered biochemical properties. Proc Natl Acad Sci 119(37):2204179119","journal-title":"Proc Natl Acad Sci"},{"key":"10130_CR43","doi-asserted-by":"crossref","unstructured":"Niotis K, Akiyoshi K, Carlton C, Isaacson R (2022) Dementia prevention in clinical practice. In: Seminars in neurology, vol 42. Thieme Medical Publishers, Inc. 333 Seventh Avenue, 18th Floor, New York, NY, pp 525\u2013548","DOI":"10.1055\/s-0042-1759580"},{"key":"10130_CR44","doi-asserted-by":"crossref","unstructured":"\u00d6z\u00e7elik YB, Altan A (2023) A comparative analysis of artificial intelligence optimization algorithms for the selection of entropy-based features in the early detection of epileptic seizures. In: 2023 14th international conference on electrical and electronics engineering (ELECO). IEEE, pp 1\u20135","DOI":"10.1109\/ELECO60389.2023.10415957"},{"issue":"8","key":"10130_CR45","doi-asserted-by":"crossref","first-page":"598","DOI":"10.3390\/fractalfract7080598","volume":"7","author":"YB \u00d6z\u00e7elik","year":"2023","unstructured":"\u00d6z\u00e7elik YB, Altan A (2023) Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust ai-based model with chaotic swarm intelligence optimization and recurrent long short-term memory. Fractal Fract 7(8):598","journal-title":"Fractal Fract"},{"issue":"2","key":"10130_CR46","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.cmpb.2013.11.014","volume":"113","author":"RB Pachori","year":"2014","unstructured":"Pachori RB, Patidar S (2014) Epileptic seizure classification in eeg signals using second-order difference plot of intrinsic mode functions. Comput Methods Programs Biomed 113(2):494\u2013502","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"10130_CR47","doi-asserted-by":"crossref","first-page":"51","DOI":"10.55730\/1300-0632.4055","volume":"32","author":"M Pekkaya","year":"2024","unstructured":"Pekkaya M, Uysal Z, Altan A, Karasu S (2024) Artificial intelligence-based evaluation of the factors affecting the sales of an iron and steel company. Turk J Electr Eng Comput Sci 32(1):51\u201367","journal-title":"Turk J Electr Eng Comput Sci"},{"issue":"04","key":"10130_CR48","doi-asserted-by":"crossref","first-page":"2350021","DOI":"10.1142\/S0129065723500211","volume":"33","author":"E Perez-Valero","year":"2023","unstructured":"Perez-Valero E, Morillas C, Lopez-Gordo MA, Minguillon J (2023) Supporting the detection of early alzheimer\u2019s disease with a four-channel eeg analysis. Int J Neural Syst 33(04):2350021","journal-title":"Int J Neural Syst"},{"key":"10130_CR49","doi-asserted-by":"crossref","unstructured":"Richman JS, Lake DE, Moorman JR (2004) Sample entropy. In: Methods in enzymology, vol 384. Elsevier, pp 172\u2013184","DOI":"10.1016\/S0076-6879(04)84011-4"},{"key":"10130_CR50","doi-asserted-by":"crossref","unstructured":"Rizal A, Hadiyoso S (2018) Sample entropy on multidistance signal level difference for epileptic eeg classification. Sci World J 2018","DOI":"10.1155\/2018\/8463256"},{"key":"10130_CR51","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102389","volume":"65","author":"N Salankar","year":"2021","unstructured":"Salankar N, Mishra P, Garg L (2021) Emotion recognition from eeg signals using empirical mode decomposition and second-order difference plot. Biomed Signal Process Control 65:102389","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"10130_CR52","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.bbe.2021.12.009","volume":"42","author":"N Salankar","year":"2022","unstructured":"Salankar N, Qaisar SM, P\u0142awiak P, Tadeusiewicz R, Hammad M (2022) Eeg based alcoholism detection by oscillatory modes decomposition second order difference plots and machine learning. Biocybern Biomed Eng 42(1):173\u2013186","journal-title":"Biocybern Biomed Eng"},{"issue":"10284","key":"10130_CR53","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","volume":"397","author":"P Scheltens","year":"2021","unstructured":"Scheltens P, De Strooper B, Kivipelto M, Holstege H, Ch\u00e9telat G, Teunissen CE, Cummings J, Flier WM (2021) Alzheimer\u2019s disease. The Lancet 397(10284):1577\u20131590","journal-title":"The Lancet"},{"issue":"7","key":"10130_CR54","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/JBHI.2020.3045718","volume":"25","author":"J Shen","year":"2020","unstructured":"Shen J, Zhang X, Huang X, Wu M, Gao J, Lu D, Ding Z, Hu B (2020) An optimal channel selection for eeg-based depression detection via kernel-target alignment. IEEE J Biomed Health Inform 25(7):2545\u20132556","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"10130_CR55","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.jneumeth.2012.07.003","volume":"210","author":"Y Song","year":"2012","unstructured":"Song Y, Crowcroft J, Zhang J (2012) Automatic epileptic seizure detection in eegs based on optimized sample entropy and extreme learning machine. J Neurosci Methods 210(2):132\u2013146","journal-title":"J Neurosci Methods"},{"issue":"6","key":"10130_CR56","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TPAMI.2007.1093","volume":"29","author":"Y Sun","year":"2007","unstructured":"Sun Y (2007) Iterative relief for feature weighting: algorithms, theories, and applications. IEEE Trans Pattern Anal Mach Intell 29(6):1035\u20131051","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"16","key":"10130_CR57","doi-asserted-by":"crossref","first-page":"8972","DOI":"10.3390\/ijms23168972","volume":"23","author":"E Sun","year":"2022","unstructured":"Sun E, Motolani A, Campos L, Lu T (2022) The pivotal role of nf-kb in the pathogenesis and therapeutics of alzheimer\u2019s disease. Int J Mol Sci 23(16):8972","journal-title":"Int J Mol Sci"},{"key":"10130_CR58","unstructured":"Tian D (2017) Particle swarm optimization with chaos-based initialization for numerical optimization. Intell Autom Soft Comput:1\u201312"},{"key":"10130_CR59","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","volume":"85","author":"RJ Urbanowicz","year":"2018","unstructured":"Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189\u2013203","journal-title":"J Biomed Inform"},{"key":"10130_CR60","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11571-014-9325-x","volume":"9","author":"R Wang","year":"2015","unstructured":"Wang R, Wang J, Yu H, Wei X, Yang C, Deng B (2015) Power spectral density and coherence analysis of alzheimer\u2019s eeg. Cogn Neurodyn 9:291\u2013304","journal-title":"Cogn Neurodyn"},{"issue":"11","key":"10130_CR61","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.3390\/e24111540","volume":"24","author":"R Wang","year":"2022","unstructured":"Wang R, Wang H, Shi L, Han C, Che Y (2022) Epileptic seizure detection using geometric features extracted from sodp shape of eeg signals and asylncpso-ga. Entropy 24(11):1540","journal-title":"Entropy"},{"key":"10130_CR62","doi-asserted-by":"crossref","first-page":"1160534","DOI":"10.3389\/fnagi.2023.1160534","volume":"15","author":"R Wang","year":"2023","unstructured":"Wang R, Wang H, Shi L, Han C, He Q, Che Y, Luo L (2023) A novel framework of mopso-gdm in recognition of alzheimer\u2019s eeg-based functional network. Front Aging Neurosci 15:1160534","journal-title":"Front Aging Neurosci"},{"key":"10130_CR63","doi-asserted-by":"crossref","unstructured":"Wang R, He Q, Han C, Wang H, Shi L, Che Y (2023) A deep learning framework for identifying alzheimer\u2019s disease using fmri-based brain network. Front Neurosci 17","DOI":"10.3389\/fnins.2023.1177424"},{"key":"10130_CR64","doi-asserted-by":"crossref","unstructured":"Wang R, Wang J, Li S, Yu H, Deng B, Wei X (2015) Multiple feature extraction and classification of electroencephalograph signal for alzheimers\u2019 with spectrum and bispectrum. Chaos Interdiscip J Nonlinear Sci 25(1)","DOI":"10.1063\/1.4906038"},{"key":"10130_CR65","doi-asserted-by":"crossref","unstructured":"Wang R, Wang J, Yu H, Wei X, Yang C, Deng B (2014) Decreased coherence and functional connectivity of electroencephalograph in alzheimer\u2019s disease. Chaos Interdiscip J Nonlinear Sci 24(3)","DOI":"10.1063\/1.4896095"},{"key":"10130_CR66","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.swevo.2018.08.015","volume":"44","author":"G Wu","year":"2019","unstructured":"Wu G, Mallipeddi R, Suganthan PN (2019) Ensemble strategies for population-based optimization algorithms-a survey. Swarm Evol Comput 44:695\u2013711","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"10130_CR67","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.artmed.2012.02.001","volume":"55","author":"J Yang","year":"2012","unstructured":"Yang J, Singh H, Hines EL, Schlaghecken F, Iliescu DD, Leeson MS, Stocks NG (2012) Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif Intell Med 55(2):117\u2013126","journal-title":"Artif Intell Med"},{"key":"10130_CR68","volume":"15","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Cui L, Huang Y, Chen Y, Li Y, Guo Q (2021) Changes of regional neural activity homogeneity in preclinical alzheimer\u2019s disease: compensation and dysfunction. Front Neurosci 15:646414","journal-title":"Front Neurosci"},{"key":"10130_CR69","volume":"174","author":"T Zhang","year":"2021","unstructured":"Zhang T, Han Z, Chen X, Chen W (2021) Quantifying randomness and complexity of a signal via maximum fuzzy membership difference entropy. Measurement 174:109053","journal-title":"Measurement"}],"container-title":["Cognitive Neurodynamics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-024-10130-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11571-024-10130-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-024-10130-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T10:05:23Z","timestamp":1731578723000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11571-024-10130-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,10]]},"references-count":69,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["10130"],"URL":"https:\/\/doi.org\/10.1007\/s11571-024-10130-z","relation":{},"ISSN":["1871-4080","1871-4099"],"issn-type":[{"value":"1871-4080","type":"print"},{"value":"1871-4099","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,10]]},"assertion":[{"value":"20 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}