[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127552-en":3,"doc-seo-127552-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127552,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders","Gait disorders are common in neurodegenerative diseases, and distinguishing similar movement patterns linked to different pathologies remains difficult even for experienced clinicians. Muscle activity generates gait kinematics, so intrinsic features of EMG activation patterns may support differential classification. This study tested whether machine learning can differentiate healthy subjects from patients with different gait disorders using surface EMG recorded from five leg muscles across multiple walking trials.","Edited by:  \nMaurizio Ferrarin,  \nFondazione Don Carlo Gnocchi Onlus (IRCCS), Italy  \nReviewed by:  \nAndrea Mannini, Sant’Anna School of Advanced  \nStudies, Italy Taian Martins Vieira, Politecnico di Torino, Italy  \n*Correspondence:  \nChristopher Fricke  \nchristopher.fricke@ [medizin.uni-leipzig.de](medizin.uni-leipzig.de)  \n† These authors have contributed equally to this work  \nSpecialty section:  \nThis article was submitted to Movement Disorders, a section of the journal Frontiers in Neurology  \nReceived: 10 February 2021  \nAccepted: 15 April 2021  \nPublished: 21 May 2021  \nCitation:  \nFricke C, Alizadeh J, Zakhary N, Woost TB, Bogdan M and Classen J (2021) Evaluation of Three Machine Learning Algorithms for the Automatic Classi􀀀cation of EMG Patterns in Gait Disorders. Front. Neurol. 12:666458.  \ndoi: 10.3389/fneur.2021.666458  \nEvaluation of Three Machine Learning Algorithms for the Automatic Classi􀀀cation of EMG Patterns in Gait Disorders  \nChristopher Fricke 1*†, Jalal Alizadeh 1,2†, Nahrin Zakhary 1, Timo B. Woost 1,3, Martin Bogdan 2 and Joseph Classen 1  \n1 Department of Neurology, University Hospital of Leipzig, Leipzig, Germany, 2 Faculty of Mathematics and Computer Science, Leipzig University, Leipzig, Germany, 3 Department of Psychiatry and Psychotherapy, Center for Psychosocial Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany  \nGait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28 .2 􀀆 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66 .2 􀀆 14.7 years) resulting from different neurological diseases walked down a hallway 10 times ata convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total) . Gait disorders were classi􀀀ed as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classi􀀀cation methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classi􀀀ers, the automatic classi􀀀cation of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91 .9%), but not SVM (accuracy 67 .6%) or KNN (accuracy 48 .7%) . For classi􀀀cation of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51 .4%, KNN 32 .4%) . These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classi􀀀cation with respect to the underlying disorder even when classi􀀀ers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation.  \nKeywords: machine learning, gait disorder classi􀀀cation, convolutional neural network, support vector machine, k nearest neighbor  \nINTRODUCTION  \nGait disorders are a common accompaniment of many neurological diseases (1) . They represent a major health hazard a","cbCaiaLzxcC2KnV0","https://ap.wps.com/l/cbCaiaLzxcC2KnV0","pdf",1181707,1,11,"English","en",105,"# Evaluation of Three Machine Learning Algorithms for Automatic EMG Classification\n## Study aim and rationale\n## Participants and EMG data acquisition\n## Ground-truth labeling of gait disorder types\n## Machine learning classifiers and evaluation approach\n## Key results and implications","[{\"question\":\"What problem does the study address?\",\"answer\":\"Accurate classification of gait disorders is challenging because patients can show similar kinematic patterns associated with different neurological entities.\"},{\"question\":\"Which data and recording setup were used for the analysis?\",\"answer\":\"Surface EMG signals were recorded from five muscles per leg (10 channels total) while participants walked multiple times in a hallway.\"},{\"question\":\"Which machine learning model performed best?\",\"answer\":\"A convolutional neural network (CNN) achieved the highest accuracy for distinguishing healthy versus patient EMG patterns and also performed best for hypokinetic versus ataxic versus normal gait.\"}]","Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders | 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problem does the study address?","Question",{"text":76,"@type":77},"Accurate classification of gait disorders is challenging because patients can show similar kinematic patterns associated with different neurological entities.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data and recording setup were used for the analysis?",{"text":81,"@type":77},"Surface EMG signals were recorded from five muscles per leg (10 channels total) while participants walked multiple times in a hallway.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performed best?",{"text":85,"@type":77},"A convolutional neural network (CNN) achieved the highest accuracy for distinguishing healthy versus patient EMG patterns and also performed best for hypokinetic versus ataxic versus normal 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