{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:05:14Z","timestamp":1779361514763,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T00:00:00Z","timestamp":1649721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T00:00:00Z","timestamp":1649721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802076"],"award-info":[{"award-number":["61802076"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61632009"],"award-info":[{"award-number":["61632009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2017A030308006"],"award-info":[{"award-number":["2017A030308006"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012269","name":"Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province","doi-asserted-by":"crossref","award":["2016ZJ01"],"award-info":[{"award-number":["2016ZJ01"]}],"id":[{"id":"10.13039\/501100012269","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100008111","name":"Hainan Provincial Department of Science and Technology","doi-asserted-by":"crossref","award":["619MS057"],"award-info":[{"award-number":["619MS057"]}],"id":[{"id":"10.13039\/501100008111","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s12652-022-03825-w","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T08:05:47Z","timestamp":1649750747000},"page":"13571-13584","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Prediction of Parkinson\u2019s disease based on artificial neural networks using speech datasets"],"prefix":"10.1007","volume":"14","author":[{"given":"Wei","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jierong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1653-4501","authenticated-orcid":false,"given":"Tao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valentina Emilia","family":"Balas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oana","family":"Geman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hung-Wen","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,12]]},"reference":[{"key":"3825_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2019.2940900","volume":"7","author":"L Ali","year":"2019","unstructured":"Ali L, Zhu C, Zhang Z, Liu Y (2019) Automated detection of Parkinson\u2019s disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimized neural network. IEEE J Transl Eng Health Med 7:1\u201310","journal-title":"IEEE J Transl Eng Health Med"},{"issue":"2","key":"3825_CR9","doi-asserted-by":"publisher","first-page":"47","DOI":"10.2478\/v10136-012-0031-x","volume":"11","author":"F Amato","year":"2013","unstructured":"Amato F, L\u00f3pez A, Pe\u00f1a-M\u00e9ndez EM (2013) Artificial neural networks in medical diagnosis. J Appl Biomed 11(2):47\u201358","journal-title":"J Appl Biomed"},{"key":"3825_CR2","doi-asserted-by":"crossref","unstructured":"Ardiansyah S, Majid MA, Zain JM (2016) Knowledge of extraction from trained neural network by using decision tree. In: 2016 2nd international conference on science in information technology (ICSITech). IEEE, pp 220\u2013225","DOI":"10.1109\/ICSITech.2016.7852637"},{"key":"3825_CR3","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1016\/S0140-6736(21)00218-X","volume":"397","author":"BR Bastiaan","year":"2021","unstructured":"Bastiaan BR, Okun MS, Christine K (2021) Parkinson\u2019s disease. Lancet 397:2284\u20132303","journal-title":"Lancet"},{"key":"3825_CR4","first-page":"6837498","volume":"2016","author":"M Behroozi","year":"2016","unstructured":"Behroozi M, Sami A (2016) A multiple-classifier framework for Parkinson\u2019s disease detection based on various vocal tests. Int J Telemed Appl 2016:6837498","journal-title":"Int J Telemed Appl"},{"issue":"6","key":"3825_CR5","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.irbm.2017.10.002","volume":"38","author":"A Benba","year":"2017","unstructured":"Benba A, Jilbab A, Hammouch A (2017) Using human factor cepstral coefficient on multiple types of voice recordings for detecting patients with Parkinson\u2019s disease. IRBM 38(6):346\u2013351","journal-title":"IRBM"},{"issue":"1","key":"3825_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3390\/s19010016","volume":"19","author":"L Berus","year":"2019","unstructured":"Berus L, Klancnik S, Brezocnik M, Ficko M (2019) Classifying Parkinson\u2019s disease based on acoustic measures using artificial neural networks. Sensors 19(1):16","journal-title":"Sensors"},{"key":"3825_CR7","doi-asserted-by":"crossref","unstructured":"Caesarendra W, Ariyanto M, Setiawan JD, Arozi M, Chang CR (2014) A pattern recognition method for stage classification of parkinson\u2019s disease utilizing voice features. In: 2014 IEEE conference on biomedical engineering and sciences (IECBES). IEEE, pp 87\u201392","DOI":"10.1109\/IECBES.2014.7047636"},{"key":"3825_CR8","doi-asserted-by":"crossref","unstructured":"Chiuchisan I, Geman O, Chiuchisan I, Iuresi AC, Graur A (2014) Neuroparkinscreen\u2014a health care system for neurological disorders screening and rehabilitation. In: 2014 international conference and exposition on electrical and power engineering (EPE). IEEE, pp 536\u2013540","DOI":"10.1109\/ICEPE.2014.6969966"},{"issue":"5","key":"3825_CR10","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1044\/2016_JSLHR-S-15-0197","volume":"59","author":"K De Keyser","year":"2016","unstructured":"De Keyser K, Santens P, Bockstael A, Botteldooren D, Talsma D, De Vos S, Van Cauwenberghe M, Verheugen F, Corthals P, De Letter M (2016) The relationship between speech production and speech perception deficits in Parkinson\u2019s disease. J Speech Lang Hear Res 59(5):915\u2013931","journal-title":"J Speech Lang Hear Res"},{"key":"3825_CR11","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.comcom.2020.08.011","volume":"162","author":"O Deperlioglu","year":"2020","unstructured":"Deperlioglu O, Kose U, Gupta D, Khanna A, Sangaiah AK (2020) Diagnosis of heart diseases by a secure internet of health things system based on autoencoder deep neural network. Comput Commun 162:31\u201350","journal-title":"Comput Commun"},{"key":"3825_CR12","doi-asserted-by":"publisher","first-page":"3541","DOI":"10.1007\/978-3-319-57111-9_1836","volume-title":"Encyclopedia of clinical neuropsychology","author":"J Fish","year":"2018","unstructured":"Fish J (2018) Encyclopedia of clinical neuropsychology. Unified Parkinson\u2019s disease rating scale. Springer, New York, pp 3541\u20133543","edition":"Unified Parkins"},{"key":"3825_CR13","doi-asserted-by":"crossref","unstructured":"Frid A, Kantor A, Svechin D, Manevitz LM (2016) Diagnosis of Parkinson\u2019s disease from continuous speech using deep convolutional networks without manual selection of features. In: 2016 IEEE international conference on the science of electrical engineering (ICSEE). IEEE, pp 1\u20134","DOI":"10.1109\/ICSEE.2016.7806118"},{"key":"3825_CR14","doi-asserted-by":"crossref","unstructured":"Geman O, Chiuchisan O (2015) Deep brain stimulation efficiency and Parkinson\u2019s disease stage prediction using Markov models. In: 2015 E-health and bioengineering conference (EHB). IEEE, pp 1\u20134","DOI":"10.1109\/EHB.2015.7391433"},{"key":"3825_CR15","doi-asserted-by":"publisher","first-page":"115540","DOI":"10.1109\/ACCESS.2019.2936564","volume":"7","author":"H Gunduz","year":"2019","unstructured":"Gunduz H (2019) Deep learning-based Parkinson\u2019s disease classification using vocal feature sets. IEEE Access 7:115540\u2013115551","journal-title":"IEEE Access"},{"key":"3825_CR16","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.cogsys.2018.06.006","volume":"52","author":"D Gupta","year":"2018","unstructured":"Gupta D, Julka A, Jain S, Aggarwal T, Khanna A, Arunkumar N, de Albuquerque VH (2018) Optimized cuttlefish algorithm for diagnosis of Parkinson\u2019s disease. Cogn Syst Res 52:36\u201348","journal-title":"Cogn Syst Res"},{"key":"3825_CR17","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/j.compeleceng.2018.04.014","volume":"68","author":"D Gupta","year":"2018","unstructured":"Gupta D, Sundaram S, Khanna A, Hassanien AE, De Albuquerque VH (2018) Improved diagnosis of Parkinson\u2019s disease using optimized crow search algorithm. Comput Electr Eng 68:412\u2013424","journal-title":"Comput Electr Eng"},{"issue":"1","key":"3825_CR18","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s13534-017-0051-2","volume":"8","author":"S Lahmiri","year":"2018","unstructured":"Lahmiri S, Dawson DA, Shmuel A (2018) Performance of machine learning methods in diagnosing Parkinson\u2019s disease based on dysphonia measures. Biomed Eng Lett 8(1):29\u201339","journal-title":"Biomed Eng Lett"},{"key":"3825_CR19","doi-asserted-by":"crossref","unstructured":"Li Y, Zhang C, Jia Y, Wang P, Zhang X, Xie T (2017) Simultaneous learning of speech feature and segment for classification of Parkinson disease. In: 2017 IEEE 19th international conference on e-health networking, applications and services (Healthcom). IEEE, pp 1\u20136","DOI":"10.1109\/HealthCom.2017.8210820"},{"key":"3825_CR20","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.compeleceng.2018.11.009","volume":"73","author":"D Malathi","year":"2019","unstructured":"Malathi D, Logesh R, Subramaniyaswamy V, Vijayakumar V, Sangaiah AK (2019) Hybrid reasoning-based privacy-aware disease prediction support system. Comput Electr Eng 73:114\u2013127","journal-title":"Comput Electr Eng"},{"key":"3825_CR21","doi-asserted-by":"crossref","unstructured":"Mostafa SA, Mustapha A, Khaleefah SH, Ahmad MS, Mohammed MA (2018) Evaluating the performance of three classification methods in diagnosis of Parkinson\u2019s disease. In: International conference on soft computing and data mining. Springer, pp 43\u201352","DOI":"10.1007\/978-3-319-72550-5_5"},{"key":"3825_CR22","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.cogsys.2018.12.004","volume":"54","author":"SA Mostafa","year":"2019","unstructured":"Mostafa SA, Mustapha A, Mohammed MA, Hamed RI, Arunkumar N, Abd G, Mohd K, Jaber MM, Khaleefah SH (2019) Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson\u2019s disease. Cogn Syst Res 54:90\u201399","journal-title":"Cogn Syst Res"},{"key":"3825_CR23","doi-asserted-by":"crossref","unstructured":"Movement Disorder Society Task\u00a0Force on\u00a0Rating Scales\u00a0for Parkinson\u2019s\u00a0Disease (2003) The unified Parkinson\u2019s disease rating scale (updrs): status and recommendations. Mov Disord 18(7):738\u2013750","DOI":"10.1002\/mds.10473"},{"key":"3825_CR24","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.artmed.2018.08.007","volume":"95","author":"CR Pereira","year":"2019","unstructured":"Pereira CR, Pereira DR, Weber SAT, Hook C, de\u00a0Albuquerque VHC, Papa JP (2019) A survey on computer-assisted Parkinson\u2019s disease diagnosis. Artif Intell Med 95:48\u201363","journal-title":"Artif Intell Med"},{"issue":"2","key":"3825_CR25","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s10462-016-9526-2","volume":"49","author":"B P\u00e9rez-S\u00e1nchez","year":"2018","unstructured":"P\u00e9rez-S\u00e1nchez B, Fontenla-Romero O, Guijarro-Berdi\u00f1as B (2018) A review of adaptive online learning for artificial neural networks. Artif Intell Rev 49(2):281\u2013299","journal-title":"Artif Intell Rev"},{"issue":"16","key":"3825_CR26","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1503\/cmaj.151179","volume":"188","author":"P Rizek","year":"2016","unstructured":"Rizek P, Kumar N, Jog MS (2016) An update on the diagnosis and treatment of Parkinson disease. CMAJ 188(16):1157\u20131165","journal-title":"CMAJ"},{"key":"3825_CR27","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.parkreldis.2018.11.011","volume":"61","author":"C Robert","year":"2018","unstructured":"Robert C, Wilson CS, Lipton RB, Arreto C-D (2018) Parkinson\u2019s disease: evolution of the scientific literature from 1983 to 2017 by countries and journals. Parkinsonism Relat Disord 61:10\u201318","journal-title":"Parkinsonism Relat Disord"},{"issue":"4","key":"3825_CR28","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/JBHI.2013.2245674","volume":"17","author":"ME Isenkul","year":"2013","unstructured":"Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O (2013) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform 17(4):828\u2013834","journal-title":"IEEE J Biomed Health Inform"},{"key":"3825_CR29","doi-asserted-by":"publisher","first-page":"101788","DOI":"10.1016\/j.artmed.2019.101788","volume":"103","author":"AK Sangaiah","year":"2020","unstructured":"Sangaiah AK, Arumugam M, Bian G-B (2020) An intelligent learning approach for improving ecg signal classification and arrhythmia analysis. Artif Intell Med 103:101788","journal-title":"Artif Intell Med"},{"key":"3825_CR30","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.comcom.2020.02.049","volume":"154","author":"AK Sangaiah","year":"2020","unstructured":"Sangaiah AK, Dhanaraj JSA, Mohandas P, Castiglione A (2020) Cognitive iot system with intelligence techniques in sustainable computing environment. Comput Commun 154:347\u2013360","journal-title":"Comput Commun"},{"key":"3825_CR31","doi-asserted-by":"publisher","first-page":"107528","DOI":"10.1016\/j.apacoust.2020.107528","volume":"171","author":"Z Soumaya","year":"2020","unstructured":"Soumaya Z, Taoufiq BD, Benayad N, Yunus K, Abdelkrim A (2020) The detection of Parkinson disease using the genetic algorithm and svm classifier. Appl Acoust 171:107528","journal-title":"Appl Acoust"},{"key":"3825_CR32","doi-asserted-by":"crossref","unstructured":"Toderean R, Geman O, Chiuchisan I, Balas VE, Beiu V (2016) Novel method for neurodegenerative disorders screening patients using hurst coefficients on eeg delta rhythm. In: International workshop soft computing applications. Springer, pp 349\u2013358","DOI":"10.1007\/978-3-319-62521-8_29"},{"issue":"5","key":"3825_CR33","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/TBME.2012.2183367","volume":"59","author":"A Tsanas","year":"2012","unstructured":"Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO (2012) Novel speech signal processing algorithms for high-accuracy classification of Parkinson\u2019s disease. IEEE Trans Biomed Eng 59(5):1264\u20131271","journal-title":"IEEE Trans Biomed Eng"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-03825-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-022-03825-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-022-03825-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T18:37:48Z","timestamp":1692729468000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-022-03825-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,12]]},"references-count":33,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["3825"],"URL":"https:\/\/doi.org\/10.1007\/s12652-022-03825-w","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,12]]},"assertion":[{"value":"23 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors. In this experiment, we did not collect any samples of human and animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human participants or animals"}}]}}