Pub Date : 2010-01-01DOI: 10.1016/S1567-424X(10)61011-2
M. Nuwer, C. Lücking
{"title":"1985–1990: President: John E. Desmedt","authors":"M. Nuwer, C. Lücking","doi":"10.1016/S1567-424X(10)61011-2","DOIUrl":"https://doi.org/10.1016/S1567-424X(10)61011-2","url":null,"abstract":"","PeriodicalId":85606,"journal":{"name":"Supplements to Clinical neurophysiology","volume":"61 1","pages":"69-78"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1567-424X(10)61011-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"56883479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Wave length and action potentials: history of the International Federation of Clinical Neurophysiology.","authors":"Marc R Nuwer, Carl H Lücking","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":85606,"journal":{"name":"Supplements to Clinical neurophysiology","volume":"61 ","pages":"3-280"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29277103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-01-01DOI: 10.1016/S1567-424X(10)61003-3
M. Nuwer, C. Lücking
{"title":"1953–1957: President: W. Grey Walter","authors":"M. Nuwer, C. Lücking","doi":"10.1016/S1567-424X(10)61003-3","DOIUrl":"https://doi.org/10.1016/S1567-424X(10)61003-3","url":null,"abstract":"","PeriodicalId":85606,"journal":{"name":"Supplements to Clinical neurophysiology","volume":"61 1","pages":"15-17"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S1567-424X(10)61003-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"56882947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1016/s1567-424x(08)00018-4
Shaun G Boe, Daniel W Stashuk, Timothy J Doherty
{"title":"Motor unit number estimates, quantitative motor unit analysis and clinical outcome measures in amyotrophic lateral sclerosis.","authors":"Shaun G Boe, Daniel W Stashuk, Timothy J Doherty","doi":"10.1016/s1567-424x(08)00018-4","DOIUrl":"https://doi.org/10.1016/s1567-424x(08)00018-4","url":null,"abstract":"","PeriodicalId":85606,"journal":{"name":"Supplements to Clinical neurophysiology","volume":"60 ","pages":"181-8"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/s1567-424x(08)00018-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29191145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1016/s1567-424x(08)00022-6
Janka Hegedus, Kelvin E Jones, Tessa Gordon
We have used a technique to estimate the number of functioning motor units (MUNE) innervating a muscle in mice based on twitch tension. The MUNE technique was verified by modeling twitch tensions from isolated ventral root stimulation. Analysis by twitch tensions allowed us to identify motor unit fiber types. The MUNE technique was used to compare normal mice with transgenic superoxide dismutase-1 mutation (G94A) mice to assess the time course of motor unit loss with respect to fiber type. Motor unit loss was found to occur well in advance of behavioral changes and the degree of reinnervation is dependent upon motor unit fiber types.
{"title":"Development and use of the incremental twitch subtraction MUNE method in mice.","authors":"Janka Hegedus, Kelvin E Jones, Tessa Gordon","doi":"10.1016/s1567-424x(08)00022-6","DOIUrl":"https://doi.org/10.1016/s1567-424x(08)00022-6","url":null,"abstract":"<p><p>We have used a technique to estimate the number of functioning motor units (MUNE) innervating a muscle in mice based on twitch tension. The MUNE technique was verified by modeling twitch tensions from isolated ventral root stimulation. Analysis by twitch tensions allowed us to identify motor unit fiber types. The MUNE technique was used to compare normal mice with transgenic superoxide dismutase-1 mutation (G94A) mice to assess the time course of motor unit loss with respect to fiber type. Motor unit loss was found to occur well in advance of behavioral changes and the degree of reinnervation is dependent upon motor unit fiber types.</p>","PeriodicalId":85606,"journal":{"name":"Supplements to Clinical neurophysiology","volume":"60 ","pages":"209-17"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/s1567-424x(08)00022-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29191149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1016/s1567-424x(08)00023-8
Kevin C McGill
{"title":"Issues and expectations for EMG decomposition.","authors":"Kevin C McGill","doi":"10.1016/s1567-424x(08)00023-8","DOIUrl":"https://doi.org/10.1016/s1567-424x(08)00023-8","url":null,"abstract":"","PeriodicalId":85606,"journal":{"name":"Supplements to Clinical neurophysiology","volume":"60 ","pages":"221-9"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/s1567-424x(08)00023-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29191150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1016/s1567-424x(08)00025-1
L J Pino, D W Stashuk, S G Boe, T J Doherty
For clinicians to use quantitative electromyography (QEMG) to help determine the presence or absence of neuromuscular disease, they must manually interpret an exhaustive set of motor unit potential (MUP) or interference pattern statistics to formulate a clinically useful muscle characterization. A new method is presented for automatically categorizing a set of quantitative electromyographic (EMG) data as characteristic of data acquired from a muscle affected by a myopathic, normal or neuropathic disease process, based on discovering patterns of MUP feature values. From their numbers of occurrence in a set of training data, representative of each muscle category, discovered patterns of MUP feature values are expressed as conditional probabilities of detecting such MUPs in each category of muscle. The conditional probabilities of each MUP in a set of MUPs acquired from an examined muscle are combined using Bayes' rule to estimate conditional probabilities of the examined muscle being of each category type. Using simulated and clinical data, the ability of a "pattern discovery" based Bayesian (PD-based Bayesian) method to correctly categorize sets of test MUP data was compared to conventional methods which use data means and outliers. The simulated data were created by modeling the effects of myopathic and neuropathic diseases using a physiologically based EMG signal simulator. The clinical data was from controls and patients with known neuropathic disorders. PD-based Bayesian muscle characterization had an accuracy of 84.4% compared to 51.9% for the means and outlier based method when using all MUP features considered. PD-based Bayesian methods can accurately characterize a muscle. PD-based Bayesian muscle characterization automatically maximizes both sensitivity and specificity and provides transparent rationalizations for its characterizations. This leads to the expectation that clinicians using PD-based Bayesian muscle characterization will be provided with improved decision support compared to that provided by the status quo means and outlier based methods.
{"title":"Decision support for QEMG.","authors":"L J Pino, D W Stashuk, S G Boe, T J Doherty","doi":"10.1016/s1567-424x(08)00025-1","DOIUrl":"https://doi.org/10.1016/s1567-424x(08)00025-1","url":null,"abstract":"<p><p>For clinicians to use quantitative electromyography (QEMG) to help determine the presence or absence of neuromuscular disease, they must manually interpret an exhaustive set of motor unit potential (MUP) or interference pattern statistics to formulate a clinically useful muscle characterization. A new method is presented for automatically categorizing a set of quantitative electromyographic (EMG) data as characteristic of data acquired from a muscle affected by a myopathic, normal or neuropathic disease process, based on discovering patterns of MUP feature values. From their numbers of occurrence in a set of training data, representative of each muscle category, discovered patterns of MUP feature values are expressed as conditional probabilities of detecting such MUPs in each category of muscle. The conditional probabilities of each MUP in a set of MUPs acquired from an examined muscle are combined using Bayes' rule to estimate conditional probabilities of the examined muscle being of each category type. Using simulated and clinical data, the ability of a \"pattern discovery\" based Bayesian (PD-based Bayesian) method to correctly categorize sets of test MUP data was compared to conventional methods which use data means and outliers. The simulated data were created by modeling the effects of myopathic and neuropathic diseases using a physiologically based EMG signal simulator. The clinical data was from controls and patients with known neuropathic disorders. PD-based Bayesian muscle characterization had an accuracy of 84.4% compared to 51.9% for the means and outlier based method when using all MUP features considered. PD-based Bayesian methods can accurately characterize a muscle. PD-based Bayesian muscle characterization automatically maximizes both sensitivity and specificity and provides transparent rationalizations for its characterizations. This leads to the expectation that clinicians using PD-based Bayesian muscle characterization will be provided with improved decision support compared to that provided by the status quo means and outlier based methods.</p>","PeriodicalId":85606,"journal":{"name":"Supplements to Clinical neurophysiology","volume":"60 ","pages":"247-61"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/s1567-424x(08)00025-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29193168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-01-01DOI: 10.1016/s1567-424x(08)00006-8
Gerhard H Visser, Joleen H Blok
{"title":"The CMAP scan.","authors":"Gerhard H Visser, Joleen H Blok","doi":"10.1016/s1567-424x(08)00006-8","DOIUrl":"https://doi.org/10.1016/s1567-424x(08)00006-8","url":null,"abstract":"","PeriodicalId":85606,"journal":{"name":"Supplements to Clinical neurophysiology","volume":"60 ","pages":"65-77"},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/s1567-424x(08)00006-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29191720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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