{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T14:16:59Z","timestamp":1789827419439,"version":"4.0.1"},"reference-count":16,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100007312","name":"Cleveland Clinic Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007312","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Muscle and Nerve"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction\/Aims<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) has shown potential in analyzing electromyography (EMG) signals, but clinical applicability remains limited by studies based on small, curated datasets, and variable accuracy. This study evaluated AI performance in classifying needle electromyography (EMG) signals as muscle activity versus background\/noise\/artifact, and then in distinguishing three clinical categories: amyotrophic lateral sclerosis (ALS), myopathy, and non\u2010disease controls.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from the Cleveland Clinic Foundation EMG Database (CCFDB), a large clinically acquired EMG dataset was utilized for this study. A two\u2010step classification approach was used: a convolutional neural network (CNN) to separate muscle activity from background\/noise\/artifact, followed by a random forest algorithm and CNNs for clinical category classification. Feature extraction techniques included Short\u2010Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), and Wavelet Packet Decomposition (WPD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>EMG data from 608 participants (266 ALS, 89 myopathy, 253 non\u2010disease controls), totaling 11,456 muscle recordings, and 15,613 segments of muscle activity were included. The muscle activity detection model achieved 85.4% accuracy. For clinical category classification, CWT with a two\u2010layer CNN performed best on the CCFDB (62% accuracy). Deeper CNN architectures did not consistently improve performance. On the publicly available curated EMGlab dataset, the best accuracy using the same AI models was higher (91%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>AI can assist in EMG analysis, but the performance gap between curated and clinically\u2010acquired datasets underscores the need for robust models capable of handling signal variability and complexity in authentic clinical contexts. Future efforts should focus on clinically\u2010oriented AI development to improve translational applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mus.70087","type":"journal-article","created":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T07:39:58Z","timestamp":1764401998000},"page":"319-328","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Electromyography Signal Classification With Artificial Intelligence for Detection of Neuromuscular Disorders Using a Large Clinically\u2010Acquired Database"],"prefix":"10.1002","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8629-5066","authenticated-orcid":false,"given":"Mohamed","family":"Taha","sequence":"first","affiliation":[{"name":"Neurology Division Massachusetts General Hospital  Boston Massachusetts USA"},{"name":"Clinical Instructor Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuaiqi","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences Cleveland Clinic Foundation  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences Cleveland Clinic Foundation  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulhamit","family":"Subasi","sequence":"additional","affiliation":[{"name":"Information Sciences &amp; Technology Department., College of Emergency Preparedness, Homeland Security &amp; Cybersecurity University at Albany  Albany New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-9547","authenticated-orcid":false,"given":"John A.","family":"Morren","sequence":"additional","affiliation":[{"name":"Neuromuscular Center Neurological Institute, Cleveland Clinic  Cleveland Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mus.28023"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103037"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103098"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042\u2010018\u20106561\u20109"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2015.2454503"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2023.10.008"},{"key":"e_1_2_12_8_1","unstructured":"M.Nikolic \u201cDetailed Analysis of Clinical Electromyography Signals EMG Decomposition Findings and Firing Pattern Analysis in Controls and Patients With Myopathy and Amytrophic Lateral Sclerosis\u201d. PhD Thesis. University of Copenhagen 2001."},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1050\u20106411(00)00025\u20100"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107079"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119825883"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/86.867887"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2006.03.004"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1050\u20106411(00)00046\u20108"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2005.04.001"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2022.11.019"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJBET.2022.126493"}],"container-title":["Muscle &amp; Nerve"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mus.70087","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mus.70087","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mus.70087","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T08:21:19Z","timestamp":1770452479000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/mus.70087"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,28]]},"references-count":16,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["10.1002\/mus.70087"],"URL":"https:\/\/doi.org\/10.1002\/mus.70087","archive":["Portico"],"relation":{},"ISSN":["0148-639X","1097-4598"],"issn-type":[{"value":"0148-639X","type":"print"},{"value":"1097-4598","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,28]]},"assertion":[{"value":"2025-05-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}