{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T09:03:38Z","timestamp":1774775018572,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Ministry of Health Brazil"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioMed Eng OnLine"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussions<\/jats:title><jats:p>Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12938-021-00896-2","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T21:02:22Z","timestamp":1623790942000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review"],"prefix":"10.1186","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-1796","authenticated-orcid":false,"given":"Felipe","family":"Fernandes","sequence":"first","affiliation":[]},{"given":"Ingridy","family":"Barbalho","sequence":"additional","affiliation":[]},{"given":"Daniele","family":"Barros","sequence":"additional","affiliation":[]},{"given":"Ricardo","family":"Valentim","sequence":"additional","affiliation":[]},{"given":"C\u00e9sar","family":"Teixeira","sequence":"additional","affiliation":[]},{"given":"Jorge","family":"Henriques","sequence":"additional","affiliation":[]},{"given":"Paulo","family":"Gil","sequence":"additional","affiliation":[]},{"given":"M\u00e1rio","family":"Dourado J\u00fanior","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,15]]},"reference":[{"key":"896_CR1","doi-asserted-by":"publisher","unstructured":"Saadeh W, Altaf MAB, Butt SA. A wearable neuro-degenerative diseases detection system based on gait dynamics. In: 2017 IFIP\/IEEE international conference on very large scale integration (VLSI-SoC). 2017. p. 1\u20136 . https:\/\/doi.org\/10.1109\/VLSI-SoC.2017.8203488.","DOI":"10.1109\/VLSI-SoC.2017.8203488"},{"issue":"1","key":"896_CR2","doi-asserted-by":"publisher","first-page":"17071","DOI":"10.1038\/nrdp.2017.71","volume":"3","author":"O Hardiman","year":"2017","unstructured":"Hardiman O, Al-Chalabi A, Chio A, Corr EM, Logroscino G, Robberecht W, Shaw PJ, Simmons Z, van den Berg LH. Amyotrophic lateral sclerosis. Nat Rev Dis Prim. 2017;3(1):17071. https:\/\/doi.org\/10.1038\/nrdp.2017.71.","journal-title":"Nat Rev Dis Prim"},{"issue":"10107","key":"896_CR3","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.1016\/S0140-6736(17)31287-4","volume":"390","author":"MA van Es","year":"2017","unstructured":"van Es MA, Hardiman O, Chio A, Al-Chalabi A, Pasterkamp RJ, Veldink JH, van den Berg LH. Amyotrophic lateral sclerosis. Lancet. 2017;390(10107):2084\u201398. https:\/\/doi.org\/10.1016\/S0140-6736(17)31287-4.","journal-title":"Lancet"},{"issue":"12","key":"896_CR4","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1016\/S1474-4422(14)70167-X","volume":"13","author":"A Chi\u00f2","year":"2014","unstructured":"Chi\u00f2 A, Pagani M, Agosta F, Calvo A, Cistaro A, Filippi M. Neuroimaging in amyotrophic lateral sclerosis: insights into structural and functional changes. Lancet Neurol. 2014;13(12):1228\u201340. https:\/\/doi.org\/10.1016\/S1474-4422(14)70167-X.","journal-title":"Lancet Neurol"},{"issue":"6","key":"896_CR5","first-page":"531","volume":"8","author":"SR Lima","year":"2010","unstructured":"Lima SR, Gomes KB. Esclerose lateral amiotr\u00f3fica e o tratamento com c\u00e9lulas-tronco. Rev Bras Clin Med. 2010;8(6):531\u20137.","journal-title":"Rev Bras Clin Med"},{"issue":"9769","key":"896_CR6","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1016\/S0140-6736(10)61156-7","volume":"377","author":"MC Kiernan","year":"2011","unstructured":"Kiernan MC, Vucic S, Cheah BC, Turner MR, Eisen A, Hardiman O, Burrell JR, Zoing MC. Amyotrophic lateral sclerosis. Lancet. 2011;377(9769):942\u201355. https:\/\/doi.org\/10.1016\/S0140-6736(10)61156-7.","journal-title":"Lancet"},{"issue":"3","key":"896_CR7","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/S1474-4422(13)70036-X","volume":"12","author":"MR Turner","year":"2013","unstructured":"Turner MR, Hardiman O, Benatar M, Brooks BR, Chio A, de Carvalho M, Ince PG, Lin C, Miller RG, Mitsumoto H, Nicholson G, Ravits J, Shaw PJ, Swash M, Talbot K, Traynor BJ, Van den Berg LH, Veldink JH, Vucic S, Kiernan MC. Controversies and priorities in amyotrophic lateral sclerosis. Lancet Neurol. 2013;12(3):310\u201322. https:\/\/doi.org\/10.1016\/S1474-4422(13)70036-X.","journal-title":"Lancet Neurol"},{"issue":"3","key":"896_CR8","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1111\/j.1468-1331.2011.03501.x","volume":"19","author":"PM Andersen","year":"2012","unstructured":"Andersen PM, Abrahams S, Borasio GD, de Carvalho M, Chio A, Van Damme P, Hardiman O, Kollewe K, Morrison KE, Petri S, Pradat P-F, Silani V, Tomik B, Wasner M, Weber M, The EFNS Task Force on Diagnosis and Management of Amyotrophic Lateral Sclerosis. EFNS guidelines on the clinical management of amyotrophic lateral sclerosis (MALS)\u2014revised report of an EFNS task force. Eur J Neurol. 2012;19(3):360\u201375. https:\/\/doi.org\/10.1111\/j.1468-1331.2011.03501.x.","journal-title":"Eur J Neurol"},{"issue":"2","key":"896_CR9","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1056\/NEJMra1603471","volume":"377","author":"RH Brown","year":"2017","unstructured":"Brown RH, Al-Chalabi A. Amyotrophic lateral sclerosis. N Engl J Med. 2017;377(2):162\u201372. https:\/\/doi.org\/10.1056\/NEJMra1603471.","journal-title":"N Engl J Med"},{"issue":"10","key":"896_CR10","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1007\/s00415-018-9008-3","volume":"265","author":"A Scarafino","year":"2018","unstructured":"Scarafino A, D'Errico E, Introna A, Fraddosio A, Distaso E, Tempesta I, Morea A, Mastronardi A, Leante R, Ruggieri M, Mastrapasqua M, Simone IL. Diagnostic and prognostic power of CSF Tau in amyotrophic lateral sclerosis. J Neurol. 2018;265(10):2353\u201362. https:\/\/doi.org\/10.1007\/s00415-018-9008-3.","journal-title":"J Neurol"},{"key":"896_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01425-9","volume-title":"Special issue on bio-medical signal processing for smarter mobile healthcare using big data analytics","author":"G Jeon","year":"2019","unstructured":"Jeon G, Ahmad A, Cuomo S, Wu W. Special issue on bio-medical signal processing for smarter mobile healthcare using big data analytics. New York: Springer; 2019. https:\/\/doi.org\/10.1007\/s12652-019-01425-9."},{"issue":"11","key":"896_CR12","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1001\/jama.2014.9799","volume":"312","author":"DK Horton","year":"2014","unstructured":"Horton DK, Mehta P, Antao VC. Quantifying a nonnotifiable disease in the united states: the national amyotrophic lateral sclerosis registry model. JAMA. 2014;312(11):1097\u20138.","journal-title":"JAMA"},{"issue":"3","key":"896_CR13","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1002\/(SICI)1097-4598(200003)23:3<336::AID-MUS4>3.0.CO;2-L","volume":"23","author":"CG Goetz","year":"2000","unstructured":"Goetz CG. Amyotrophic lateral sclerosis: early contributions of Jean-Martin Charcot. Muscle Nerve. 2000;23(3):336\u201343.","journal-title":"Muscle Nerve"},{"issue":"1","key":"896_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/ncomms12408","volume":"7","author":"KC Arthur","year":"2016","unstructured":"Arthur KC, Calvo A, Price TR, Geiger JT, Chio A, Traynor BJ. Projected increase in amyotrophic lateral sclerosis from 2015 to 2040. Nat Commun. 2016;7(1):1\u20136.","journal-title":"Nat Commun"},{"key":"896_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neubiorev.2019.12.032","volume":"111","author":"JP Rosa Silva","year":"2020","unstructured":"Rosa Silva JP, Santiago J\u00fanior JB, dos Santos EL, de Carvalho FO, de Fran\u00e7Costa IMP, de Mendon\u00e7a DMF. Quality of life and functional independence in amyotrophic lateral sclerosis: a systematic review. Neurosci Biobehav Rev. 2020;111:1\u201311. https:\/\/doi.org\/10.1016\/j.neubiorev.2019.12.032.","journal-title":"Neurosci Biobehav Rev"},{"key":"896_CR16","doi-asserted-by":"publisher","unstructured":"Bustamante P, Grandez K, Solas G, Arrizabalaga S. A low-cost platform for testing activities in Parkinson and ALS patients. In: The 12th IEEE international conference on e-Health networking, applications and services. 2010. p. 302\u20137. https:\/\/doi.org\/10.1109\/HEALTH.2010.5556550.","DOI":"10.1109\/HEALTH.2010.5556550"},{"issue":"18","key":"896_CR17","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1212\/WNL.0000000000007401","volume":"92","author":"K Bjornevik","year":"2019","unstructured":"Bjornevik K, Zhang Z, O'Reilly \u00c9J, Berry JD, Clish CB, Deik A, Jeanfavre S, Kato I, Kelly RS, Kolonel LN, Liang L, Marchand LL, McCullough ML, Paganoni S, Pierce KA, Schwarzschild MA, Shadyab AH, Wactawski-Wende J, Wang DD, Wang Y, Manson JE, Ascherio A. Prediagnostic plasma metabolomics and the risk of amyotrophic lateral sclerosis. Neurology. 2019;92(18):2089\u2013100. https:\/\/doi.org\/10.1212\/WNL.0000000000007401.","journal-title":"Neurology"},{"issue":"9","key":"896_CR18","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1016\/j.acra.2013.03.017","volume":"20","author":"BR Foerster","year":"2013","unstructured":"Foerster BR, Dwamena BA, Petrou M, Carlos RC, Callaghan BC, Churchill CL, Mohamed MA, Bartels C, Benatar M, Bonzano L, Ciccarelli O, Cosottini M, Ellis CM, Ehrenreich H, Filippini N, Ito M, Kalra S, Melhem ER, Pyra T, Roccatagliata L, Senda J, Sobue G, Turner MR, Feldman EL, Pomper MG. Diagnostic accuracy of diffusion tensor imaging in amyotrophic lateral sclerosis: a systematic review and individual patient data meta-analysis. Acad Radiol. 2013;20(9):1099\u2013106. https:\/\/doi.org\/10.1016\/j.acra.2013.03.017.","journal-title":"Acad Radiol"},{"issue":"11","key":"896_CR19","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1016\/S1474-4422(16)30199-5","volume":"15","author":"A Al-Chalabi","year":"2016","unstructured":"Al-Chalabi A, Hardiman O, Kiernan MC, Chi\u00f2 A, Rix-Brooks B, van den Berg LH. Amyotrophic lateral sclerosis: moving towards a new classification system. Lancet Neurol. 2016;15(11):1182\u201394. https:\/\/doi.org\/10.1016\/S1474-4422(16)30199-5.","journal-title":"Lancet Neurol"},{"issue":"3","key":"896_CR20","doi-asserted-by":"publisher","first-page":"037004","DOI":"10.1088\/2057-1976\/aa9c64","volume":"4","author":"M Fraschini","year":"2018","unstructured":"Fraschini M, Lai M, Demuru M, Puligheddu M, Floris G, Borghero G, Marrosu F. Functional brain connectivity analysis in amyotrophic lateral sclerosis: an EEG source-space study. Biomed Phys Eng Express. 2018;4(3):037004. https:\/\/doi.org\/10.1088\/2057-1976\/aa9c64.","journal-title":"Biomed Phys Eng Express"},{"key":"896_CR21","doi-asserted-by":"publisher","unstructured":"Barbalho IMP, Silva PdA, Fernandes FRdS, Neto FMM, Leite CRM. An investigation on the use of ontologies for pattern classification\u2014study applied to the monitoring of food intake. In: Proceedings of the Euro American conference on telematics and information systems. EATIS \u201918. New York: Association for Computing Machinery; 2018. https:\/\/doi.org\/10.1145\/3293614.3293627.","DOI":"10.1145\/3293614.3293627"},{"issue":"3","key":"896_CR22","doi-asserted-by":"publisher","first-page":"120","DOI":"10.23919\/SAIEE.2020.9142605","volume":"111","author":"V Aharonson","year":"2020","unstructured":"Aharonson V, Coopoo VY, Govender KL, Postema M. Automatic pupil detection and gaze estimation using the vestibulo-ocular reflex in a low-cost eye-tracking setup. SAIEE Afr Res J. 2020;111(3):120\u20134.","journal-title":"SAIEE Afr Res J"},{"key":"896_CR23","doi-asserted-by":"publisher","unstructured":"Lingegowda DR, Amrutesh K, Ramanujam S. Electrooculography based assistive technology for ALS patients. In: 2017 IEEE international conference on consumer electronics-Asia (ICCE-Asia). 2017. p. 36\u201340 . https:\/\/doi.org\/10.1109\/ICCE-ASIA.2017.8307837.","DOI":"10.1109\/ICCE-ASIA.2017.8307837"},{"issue":"4","key":"896_CR24","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s10209-011-0256-6","volume":"11","author":"A Kr\u00f3lak","year":"2012","unstructured":"Kr\u00f3lak A, Strumi\u0142\u0142o P. Eye-blink detection system for human\u2013computer interaction. Univ Access Inf Soc. 2012;11(4):409\u201319. https:\/\/doi.org\/10.1007\/s10209-011-0256-6.","journal-title":"Univ Access Inf Soc"},{"issue":"8","key":"896_CR25","doi-asserted-by":"publisher","first-page":"104854","DOI":"10.1371\/journal.pone.0104854","volume":"9","author":"J H\u00f6hne","year":"2014","unstructured":"H\u00f6hne J, Holz E, Staiger-S\u00e4lzer P, M\u00fcller K-R, K\u00fcbler A, Tangermann M. Motor imagery for severely motor-impaired patients: evidence for brain\u2013computer interfacing as superior control solution. PLoS ONE. 2014;9(8):104854.","journal-title":"PLoS ONE"},{"key":"896_CR26","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1109\/ACCESS.2016.2520093","volume":"4","author":"MA Eid","year":"2016","unstructured":"Eid MA, Giakoumidis N, El Saddik A. A novel eye-gaze-controlled wheelchair system for navigating unknown environments: case study with a person with ALS. IEEE Access. 2016;4:558\u201373.","journal-title":"IEEE Access"},{"issue":"10236","key":"896_CR27","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1016\/S0140-6736(20)30226-9","volume":"395","author":"N Schwalbe","year":"2020","unstructured":"Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579\u201386. https:\/\/doi.org\/10.1016\/S0140-6736(20)30226-9.","journal-title":"Lancet"},{"issue":"5","key":"896_CR28","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/j.compbiomed.2013.01.020","volume":"43","author":"A Subasi","year":"2013","unstructured":"Subasi A. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput Biol Med. 2013;43(5):576\u201386. https:\/\/doi.org\/10.1016\/j.compbiomed.2013.01.020.","journal-title":"Comput Biol Med"},{"issue":"7","key":"896_CR29","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.amjmed.2019.01.017","volume":"132","author":"N Noorbakhsh-Sabet","year":"2019","unstructured":"Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial intelligence transforms the future of health care. Am J Med. 2019;132(7):795\u2013801. https:\/\/doi.org\/10.1016\/j.amjmed.2019.01.017.","journal-title":"Am J Med"},{"key":"896_CR30","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3389\/fnins.2019.00135","volume":"13","author":"V Grollemund","year":"2019","unstructured":"Grollemund V, Pradat PF, Querin G, Delbot F, Le Chat G, Pradat-Peyre JF, Bede P. Machine learning in amyotrophic lateral sclerosis: achievements, pitfalls, and future directions. Front Neurosci. 2019;13:135. https:\/\/doi.org\/10.3389\/fnins.2019.00135.","journal-title":"Front Neurosci"},{"key":"896_CR31","series-title":"Developments in Biomedical Engineering and Bioelectronics","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/B978-0-12-818946-7.00001-9","volume-title":"Biomedical signal processing and artificial intelligence in healthcare","author":"N AlHinai","year":"2020","unstructured":"AlHinai N. Chapter\u2014introduction to biomedical signal processing and artificial intelligence. In: Zgallai W, editor. Biomedical signal processing and artificial intelligence in healthcare. Developments in Biomedical Engineering and Bioelectronics. Amsrerdam: Academic Press; 2020. p. 1\u201328. https:\/\/doi.org\/10.1016\/B978-0-12-818946-7.00001-9."},{"key":"896_CR32","series-title":"The Biomedical Engineering HandbookThe Biomedical Engineering HandbookThe Biomedical Engineering Handbook","first-page":"1","volume-title":"Medical devices and systems","author":"A Cohen","year":"2006","unstructured":"Cohen A. Chapter 1\u2013biomedical signals: origin and dynamic characteristics; frequency-domain analysis. In: Bronzino JD, editor. Medical devices and systems. The Biomedical Engineering HandbookThe Biomedical Engineering HandbookThe Biomedical Engineering Handbook. Boca Raton: CRC Press; 2006. p. 1\u201322."},{"key":"896_CR33","doi-asserted-by":"crossref","unstructured":"Alim OA, Moselhy M, Mroueh F. EMG signal processing and diagnostic of muscle diseases. In: 2012 2nd international conference on advances in computational tools for engineering applications (ACTEA). 2012. p. 1\u20136.","DOI":"10.1109\/ICTEA.2012.6462866"},{"issue":"9","key":"896_CR34","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1016\/S1474-4422(11)70180-6","volume":"10","author":"P Luna","year":"2011","unstructured":"Luna P. Controlling machines with just the power of thought. Lancet Neurol. 2011;10(9):780\u20131. https:\/\/doi.org\/10.1016\/S1474-4422(11)70180-6.","journal-title":"Lancet Neurol"},{"issue":"6","key":"896_CR35","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1109\/JBHI.2016.2608720","volume":"20","author":"S Chen","year":"2016","unstructured":"Chen S, Lach J, Lo B, Yang G. Toward pervasive gait analysis with wearable sensors: a systematic review. IEEE J Biomed Health Inform. 2016;20(6):1521\u201337. https:\/\/doi.org\/10.1109\/JBHI.2016.2608720.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"896_CR36","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s13311-016-0484-9","volume":"14","author":"RA Menke","year":"2017","unstructured":"Menke RA, Agosta F, Grosskreutz J, Filippi M, Turner MR. Neuroimaging endpoints in amyotrophic lateral sclerosis. Neurotherapeutics. 2017;14(1):11\u201323.","journal-title":"Neurotherapeutics"},{"key":"896_CR37","unstructured":"Kitchenham B. Procedures for performing systematic reviews. Technical report, Keele University, Department of Computer Science, Software Engineering Group and Empirical Software Engineering National ICT Australia Ltd., Keele, Staffs, ST5 5BG, UK; 2004."},{"issue":"1","key":"896_CR38","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1186\/s13643-016-0384-4","volume":"5","author":"M Ouzzani","year":"2016","unstructured":"Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan\u2014a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. https:\/\/doi.org\/10.1186\/s13643-016-0384-4.","journal-title":"Syst Rev"},{"issue":"7","key":"896_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LSENS.2019.2921072","volume":"3","author":"S Chatterjee","year":"2019","unstructured":"Chatterjee S, Samanta K, Choudhury NR, Bose R. Detection of myopathy and ALS electromyograms employing modified window Stockwell transform. IEEE Sens Lett. 2019;3(7):1\u20134. https:\/\/doi.org\/10.1109\/LSENS.2019.2921072.","journal-title":"IEEE Sens Lett"},{"issue":"1","key":"896_CR40","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TNSRE.2013.2274658","volume":"22","author":"X Zhang","year":"2014","unstructured":"Zhang X, Barkhaus PE, Rymer WZ, Zhou P. Machine learning for supporting diagnosis of amyotrophic lateral sclerosis using surface electromyogram. IEEE Trans Neural Syst Rehabilit Eng. 2014;22(1):96\u2013103. https:\/\/doi.org\/10.1109\/TNSRE.2013.2274658.","journal-title":"IEEE Trans Neural Syst Rehabilit Eng"},{"issue":"7","key":"896_CR41","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1109\/TIM.2018.2866744","volume":"68","author":"A Hazarika","year":"2019","unstructured":"Hazarika A, Dutta L, Barthakur M, Bhuyan M. A multiview discriminant feature fusion-based nonlinear process assessment and diagnosis: application to medical diagnosis. IEEE Trans Instrum Meas. 2019;68(7):2498\u2013506. https:\/\/doi.org\/10.1109\/TIM.2018.2866744.","journal-title":"IEEE Trans Instrum Meas"},{"issue":"4","key":"896_CR42","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10916-014-0031-3","volume":"38","author":"E Gokgoz","year":"2014","unstructured":"Gokgoz E, Subasi A. Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders. J Med Syst. 2014;38(4):31.","journal-title":"J Med Syst"},{"key":"896_CR43","first-page":"1","volume":"14","author":"B Ambikapathy","year":"2018","unstructured":"Ambikapathy B, Kirshnamurthy K, Venkatesan R. Assessment of electromyograms using genetic algorithm and artificial neural networks. Evolut Intell. 2018;14:1\u201311.","journal-title":"Evolut Intell"},{"issue":"2","key":"896_CR44","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/TBCAS.2014.2309252","volume":"8","author":"ABMSU Doulah","year":"2014","unstructured":"Doulah ABMSU, Fattah SA, Zhu WP, Ahmad MO. Wavelet domain feature extraction scheme based on dominant motor unit action potential of EMG signal for neuromuscular disease classification. IEEE Trans Biomed Circuits Syst. 2014;8(2):155\u201364. https:\/\/doi.org\/10.1109\/TBCAS.2014.2309252.","journal-title":"IEEE Trans Biomed Circuits Syst"},{"issue":"4","key":"896_CR45","doi-asserted-by":"publisher","first-page":"12274","DOI":"10.1111\/exsy.12274","volume":"35","author":"M Vallejo","year":"2018","unstructured":"Vallejo M, Gallego CJ, Duque-Mu\u00f1oz L, Delgado-Trejos E. Neuromuscular disease detection by neural networks and fuzzy entropy on time\u2013frequency analysis of electromyography signals. Expert Syst. 2018;35(4):12274.","journal-title":"Expert Syst"},{"key":"896_CR46","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.bspc.2014.12.005","volume":"18","author":"E Gokgoz","year":"2015","unstructured":"Gokgoz E, Subasi A. Comparison of decision tree algorithms for EMG signal classification using DWT. Biomed Signal Process Control. 2015;18:138\u201344.","journal-title":"Biomed Signal Process Control"},{"key":"896_CR47","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.bspc.2015.02.002","volume":"18","author":"Y Xia","year":"2015","unstructured":"Xia Y, Gao Q, Ye Q. Classification of gait rhythm signals between patients with neuro-degenerative diseases and normal subjects: experiments with statistical features and different classification models. Biomed Signal Process Control. 2015;18:254\u201362. https:\/\/doi.org\/10.1016\/j.bspc.2015.02.002.","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"896_CR48","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/TBME.2016.2536438","volume":"64","author":"P Ren","year":"2017","unstructured":"Ren P, Tang S, Fang F, Luo L, Xu L, Bringas-Vega ML, Yao D, Kendrick KM, Valdes-Sosa PA. Gait rhythm fluctuation analysis for neurodegenerative diseases by empirical mode decomposition. IEEE Trans Biomed Eng. 2017;64(1):52\u201360. https:\/\/doi.org\/10.1109\/TBME.2016.2536438.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"896_CR49","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1515\/bmt-2014-0089","volume":"61","author":"A Khorasani","year":"2016","unstructured":"Khorasani A, Daliri MR, Pooyan M. Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model. Biomed Eng. 2016;61(1):119\u201326.","journal-title":"Biomed Eng"},{"key":"896_CR50","doi-asserted-by":"publisher","first-page":"251","DOI":"10.3389\/fnhum.2013.00251","volume":"7","author":"R Welsh","year":"2013","unstructured":"Welsh R, Jelsone-Swain L, Foerster B. The utility of independent component analysis and machine learning in the identification of the amyotrophic lateral sclerosis diseased brain. Front Hum Neurosci. 2013;7:251. https:\/\/doi.org\/10.3389\/fnhum.2013.00251.","journal-title":"Front Hum Neurosci"},{"key":"896_CR51","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.nicl.2017.08.002","volume":"16","author":"PM Ferraro","year":"2017","unstructured":"Ferraro PM, Agosta F, Riva N, Copetti M, Spinelli EG, Falzone Y, Sorar\u00f9 G, Comi G, Chi\u00f2 A, Filippi M. Multimodal structural MRI in the diagnosis of motor neuron diseases. NeuroImage Clin. 2017;16:240\u20137.","journal-title":"NeuroImage Clin"},{"issue":"2","key":"896_CR52","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/TNSRE.2017.2728140","volume":"26","author":"R Sorbello","year":"2018","unstructured":"Sorbello R, Tramonte S, Giardina ME, La Bella V, Spataro R, Allison B, Guger C, Chella A. A human\u2013humanoid interaction through the use of BCI for locked-in ALS patients using neuro-biological feedback fusion. IEEE Trans Neural Syst Rehabilit Eng. 2018;26(2):487\u201397.","journal-title":"IEEE Trans Neural Syst Rehabilit Eng"},{"issue":"7","key":"896_CR53","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.3390\/s17071557","volume":"17","author":"Y-H Liu","year":"2017","unstructured":"Liu Y-H, Huang S, Huang Y-D. Motor imagery EEG classification for patients with amyotrophic lateral sclerosis using fractal dimension and fisher's criterion-based channel selection. Sensors. 2017;17(7):1557.","journal-title":"Sensors"},{"issue":"1","key":"896_CR54","doi-asserted-by":"publisher","first-page":"016013","DOI":"10.1088\/1741-2560\/12\/1\/016013","volume":"12","author":"BO Mainsah","year":"2015","unstructured":"Mainsah BO, Collins LM, Colwell KA, Sellers EW, Ryan DB, Caves K, Throckmorton CS. Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study. J Neural Eng. 2015;12(1):016013. https:\/\/doi.org\/10.1088\/1741-2560\/12\/1\/016013.","journal-title":"J Neural Eng"},{"issue":"1","key":"896_CR55","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s11571-019-09541-0","volume":"14","author":"Y Miao","year":"2020","unstructured":"Miao Y, Yin E, Allison BZ, Zhang Y, Chen Y, Dong Y, Wang X, Hu D, Chchocki A, Jin J. An ERP-based BCI with peripheral stimuli: validation with ALS patients. Cogn Neurodyn. 2020;14(1):21\u201333.","journal-title":"Cogn Neurodyn"},{"key":"896_CR56","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.nicl.2016.10.008","volume":"13","author":"HK van der Burgh","year":"2017","unstructured":"van der Burgh HK, Schmidt R, Westeneng H-J, de Reus MA, van den Berg LH, van den Heuvel MP. Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. NeuroImage Clin. 2017;13:361\u20139.","journal-title":"NeuroImage Clin"},{"issue":"8","key":"896_CR57","doi-asserted-by":"publisher","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","volume":"39","author":"A Phinyomark","year":"2012","unstructured":"Phinyomark A, Phukpattaranont P, Limsakul C. Feature reduction and selection for EMG signal classification. Expert Syst Appl. 2012;39(8):7420\u201331. https:\/\/doi.org\/10.1016\/j.eswa.2012.01.102.","journal-title":"Expert Syst Appl"},{"key":"896_CR58","first-page":"1","volume-title":"Brain\u2013computer interfaces","author":"B Graimann","year":"2009","unstructured":"Graimann B, Allison B, Pfurtscheller G. Brain\u2013computer interfaces: a gentle introduction. In: Brain\u2013computer interfaces. New York: Springer; 2009. p. 1\u201327."},{"key":"896_CR59","doi-asserted-by":"publisher","first-page":"217","DOI":"10.3389\/fnins.2015.00217","volume":"9","author":"C Herff","year":"2015","unstructured":"Herff C, Heger D, de Pesters A, Telaar D, Brunner P, Schalk G, Schultz T. Brain-to-text: decoding spoken phrases from phone representations in the brain. Front Neurosci. 2015;9:217. https:\/\/doi.org\/10.3389\/fnins.2015.00217.","journal-title":"Front Neurosci"},{"issue":"7753","key":"896_CR60","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41586-019-1119-1","volume":"568","author":"GK Anumanchipalli","year":"2019","unstructured":"Anumanchipalli GK, Chartier J, Chang EF. Speech synthesis from neural decoding of spoken sentences. Nature. 2019;568(7753):493\u20138. https:\/\/doi.org\/10.1038\/s41586-019-1119-1.","journal-title":"Nature"},{"key":"896_CR61","doi-asserted-by":"publisher","unstructured":"Cooney C, Folli R, Coyle D. Optimizing layers improves CNN generalization and transfer learning for imagined speech decoding from EEG. In: 2019 IEEE international conference on systems, man and cybernetics (SMC). 2019. p. 1311\u20136. https:\/\/doi.org\/10.1109\/SMC.2019.8914246.","DOI":"10.1109\/SMC.2019.8914246"},{"key":"896_CR62","doi-asserted-by":"publisher","first-page":"290","DOI":"10.3389\/fnins.2020.00290","volume":"14","author":"D Dash","year":"2020","unstructured":"Dash D, Ferrari P, Wang J. Decoding imagined and spoken phrases from non-invasive neural (meg) signals. Front Neurosci. 2020;14:290. https:\/\/doi.org\/10.3389\/fnins.2020.00290.","journal-title":"Front Neurosci"},{"key":"896_CR63","doi-asserted-by":"publisher","first-page":"2782","DOI":"10.21437\/Interspeech.2020-3071","volume":"2020","author":"D Dash","year":"2020","unstructured":"Dash D, Ferrari P, Hernandez A, Heitzman D, Austin SG, Wang J. Neural speech decoding for amyotrophic lateral sclerosis. Proc Interspeech. 2020;2020:2782\u20136. https:\/\/doi.org\/10.21437\/Interspeech.2020-3071.","journal-title":"Proc Interspeech"},{"key":"896_CR64","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1155\/2016\/7354082","volume":"2016","author":"H Tamura","year":"2016","unstructured":"Tamura H, Yan M, Sakurai K, Tanno K. EOG-sEMG human interface for communication. Intell Neurosci. 2016;2016:15. https:\/\/doi.org\/10.1155\/2016\/7354082.","journal-title":"Intell Neurosci"},{"issue":"1","key":"896_CR65","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/1475-925X-10-31","volume":"10","author":"CG Pinheiro","year":"2011","unstructured":"Pinheiro CG, Naves EL, Pino P, Losson E, Andrade AO, Bourhis G. Alternative communication systems for people with severe motor disabilities: a survey. BioMed Eng OnLine. 2011;10(1):31. https:\/\/doi.org\/10.1186\/1475-925X-10-31.","journal-title":"BioMed Eng OnLine"},{"key":"896_CR66","doi-asserted-by":"crossref","unstructured":"Hori J, Sakano K, Saitoh Y. Development of communication supporting device controlled by eye movements and voluntary eye blink. In: The 26th annual international conference of the IEEE engineering in medicine and biology society, vol. 2. 2004. p. 4302\u20135.","DOI":"10.1109\/IEMBS.2004.1404198"},{"issue":"8","key":"896_CR67","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1007\/s11760-014-0680-1","volume":"9","author":"A Fathi","year":"2015","unstructured":"Fathi A, Abdali-Mohammadi F. Camera-based eye blinks pattern detection for intelligent mouse. Signal Image Video Process. 2015;9(8):1907\u201316. https:\/\/doi.org\/10.1007\/s11760-014-0680-1.","journal-title":"Signal Image Video Process"},{"key":"896_CR68","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.compmedimag.2017.04.006","volume":"65","author":"K Harezlak","year":"2018","unstructured":"Harezlak K, Kasprowski P. Application of eye tracking in medicine: a survey, research issues and challenges. Comput Med Imaging Graph. 2018;65:176\u201390. https:\/\/doi.org\/10.1016\/j.compmedimag.2017.04.006.","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"896_CR69","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s10209-009-0149-0","volume":"8","author":"A Villanueva","year":"2009","unstructured":"Villanueva A, Daunys G, Hansen DW, B\u00f6hme M, Cabeza R, Meyer A, Barth E. A geometric approach to remote eye tracking. Univ Access Inf Soc. 2009;8(4):241. https:\/\/doi.org\/10.1007\/s10209-009-0149-0.","journal-title":"Univ Access Inf Soc"},{"issue":"2","key":"896_CR70","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s12193-014-0171-2","volume":"9","author":"Q Zhao","year":"2015","unstructured":"Zhao Q, Yuan X, Tu D, Lu J. Eye moving behaviors identification for gaze tracking interaction. J Multimodal User Interfaces. 2015;9(2):89\u2013104. https:\/\/doi.org\/10.1007\/s12193-014-0171-2.","journal-title":"J Multimodal User Interfaces"},{"issue":"3","key":"896_CR71","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s00138-018-00997-4","volume":"30","author":"Y Liu","year":"2019","unstructured":"Liu Y, Lee B-S, Rajan D, Sluzek A, McKeown MJ. CamType: assistive text entry using gaze with an off-the-shelf webcam. Mach Vis Appl. 2019;30(3):407\u201321. https:\/\/doi.org\/10.1007\/s00138-018-00997-4.","journal-title":"Mach Vis Appl"},{"issue":"6","key":"896_CR72","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1080\/17549507.2018.1508499","volume":"20","author":"J Wang","year":"2018","unstructured":"Wang J, Kothalkar PV, Kim M, Bandini A, Cao B, Yunusova Y, Campbell TF, Heitzman D, Green JR. Automatic prediction of intelligible speaking rate for individuals with ALS from speech acoustic and articulatory samples. Int J Speech Lang Pathol. 2018;20(6):669\u201379. https:\/\/doi.org\/10.1080\/17549507.2018.1508499.","journal-title":"Int J Speech Lang Pathol"},{"key":"896_CR73","doi-asserted-by":"publisher","unstructured":"Wisler A, Teplansky K, Green J, Yunusova Y, Campbell T, Heitzman D, Wang J. Speech-based estimation of bulbar regression in amyotrophic lateral sclerosis. In: Proceedings of the eighth workshop on speech and language processing for assistive technologies. Association for Computational Linguistics, Minneapolis, Minnesota; 2019. p. 24\u201331. https:\/\/doi.org\/10.18653\/v1\/W19-1704. https:\/\/www.aclweb.org\/anthology\/W19-1704.","DOI":"10.18653\/v1\/W19-1704"},{"issue":"1","key":"896_CR74","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/S0022-510X(99)00210-5","volume":"169","author":"JM Cedarbaum","year":"1999","unstructured":"Cedarbaum JM, Stambler N, Malta E, Fuller C, Hilt D, Thurmond B, Nakanishi A. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. J Neurol Sci. 1999;169(1):13\u201321. https:\/\/doi.org\/10.1016\/S0022-510X(99)00210-5.","journal-title":"J Neurol Sci"},{"issue":"12","key":"896_CR75","doi-asserted-by":"publisher","first-page":"e0167331","DOI":"10.1371\/journal.pone.0167331","volume":"11","author":"C Schuster","year":"2016","unstructured":"Schuster C, Hardiman O, Bede P. Development of an automated MRI-based diagnostic protocol for amyotrophic lateral sclerosis using disease-specific pathognomonic features: a quantitative disease-state classification study. PLoS ONE. 2016;11(12):e0167331.","journal-title":"PLoS ONE"},{"key":"896_CR76","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.21437\/Interspeech.2016-1542","volume":"2016","author":"J Wang","year":"2016","unstructured":"Wang J, Kothalkar PV, Cao B, Heitzman D. Towards automatic detection of amyotrophic lateral sclerosis from speech acoustic and articulatory samples. Interspeech. 2016;2016:1195\u20139. https:\/\/doi.org\/10.21437\/Interspeech.2016-1542.","journal-title":"Interspeech"},{"key":"896_CR77","doi-asserted-by":"publisher","unstructured":"Suhas B, Mallela J, Illa A, Yamini B, Atchayaram N, Yadav R, Gope D, Ghosh PK. Speech task based automatic classification of ALS and Parkinson\u2019s disease and their severity using log Mel spectrograms. In: 2020 international conference on signal processing and communications (SPCOM). 2020. p. 1\u20135. https:\/\/doi.org\/10.1109\/SPCOM50965.2020.9179503.","DOI":"10.1109\/SPCOM50965.2020.9179503"},{"key":"896_CR78","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.21437\/Interspeech.2018-2496","volume":"2018","author":"K An","year":"2018","unstructured":"An K, Kim M, Teplansky K, Green J, Campbell T, Yunusova Y, Heitzman D, Wang J. Automatic early detection of amyotrophic lateral sclerosis from intelligible speech using convolutional neural networks. Proc Interspeech. 2018;2018:1913\u20137. https:\/\/doi.org\/10.21437\/Interspeech.2018-2496.","journal-title":"Proc Interspeech"},{"issue":"5\u20136","key":"896_CR79","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1159\/000506259","volume":"19","author":"H Vieira","year":"2019","unstructured":"Vieira H, Costa N, Sousa T, Reis S, Coelho L. Voice-based classification of amyotrophic lateral sclerosis: where are we and where are we going? a systematic review. Neurodegener Dis. 2019;19(5\u20136):163\u201370. https:\/\/doi.org\/10.1159\/000506259.","journal-title":"Neurodegener Dis"},{"key":"896_CR80","doi-asserted-by":"publisher","DOI":"10.1044\/2020_JSLHR-20-00288","author":"A Wisler","year":"2021","unstructured":"Wisler A, Teplansky K, Heitzman D, Wang J. The effects of symptom onset location on automatic amyotrophic lateral sclerosis detection using the correlation structure of articulatory movements. J Speech Lang Hear Res. 2021. https:\/\/doi.org\/10.1044\/2020_JSLHR-20-00288.","journal-title":"J Speech Lang Hear Res"},{"key":"896_CR81","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1038\/s41746-020-00335-x","volume":"3","author":"GM Stegmann","year":"2020","unstructured":"Stegmann GM, Hahn S, Liss J, Shefner J, Rutkove S, Shelton K, Duncan CJ, Berisha V. Early detection and tracking of bulbar changes in ALS via frequent and remote speech analysis. NPJ Digit Med. 2020;3:132. https:\/\/doi.org\/10.1038\/s41746-020-00335-x.","journal-title":"NPJ Digit Med"}],"container-title":["BioMedical Engineering OnLine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-021-00896-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12938-021-00896-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-021-00896-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T21:44:43Z","timestamp":1725227083000},"score":1,"resource":{"primary":{"URL":"https:\/\/biomedical-engineering-online.biomedcentral.com\/articles\/10.1186\/s12938-021-00896-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,15]]},"references-count":81,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["896"],"URL":"https:\/\/doi.org\/10.1186\/s12938-021-00896-2","relation":{},"ISSN":["1475-925X"],"issn-type":[{"value":"1475-925X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,15]]},"assertion":[{"value":"3 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"61"}}