Canada Spends Logo

Explore our Budget 2026 analysis

Grant: Collaborative Health Research Projects

University of Waterloo

$71,356.00

Clinical characterization of muscle using quantitative electromyographic signal analysis

FY 2008-09

Summary
Neuromuscular disorders change the structure and organization of the muscles involved and their complement of motor units (MUs). Characteristics of electromyographic (EMG) signals suitably detected from affected muscles can reflect the type and severity of disorder present. Proper interpretation of signal characteristics can assist with diagnosis, treatment and management. Currently, EMG signals are subjectively and qualitatively assessed for features consistent with nerve or muscle disease, chronicity, and severity. This assessment is subjective, and in part, highly dependent on the skill and experience of the examining physician and the extent to which progressive changes can be tracked is limited. Quantitative EMG (QEMG) analysis allows for a more objective, consistent and sensitive electrophysiological characterization. However, few applications capable of clinically acquiring QEMG data exist and more importantly a method for completing the crucial step of interpreting QEMG data to produce a characterization of the neuromuscular system is currently not available. The objectives of the proposed research are therefore to produce an integrated system for EMG signal acquisition, QEMG analysis and electrophysiological characterization for routine use in an EMG clinic. In addition, a comprehensive and intuitive rationale, and a valid measure of confidence will be provided for each characterization to support improved clinical decisions. Novel aspects of the new system will include: the integration of acquisition, analysis and interpretation; the ease and consistency of signal acquisition, the validity of the QEMG data provided; and the value of the electrophysiological characterization. Beyond facilitating the acquisition and quality of QEMG data, the main significance of this work will be improved interpretation of QEMG data which will contribute to better diagnosis, treatment and management of neuromuscular disorders. Furthermore, demonstrating an ability to readily complete these studies in an EMG clinic will increase the likelihood of the methods being translated to other clinical EMG machines for widespread impact.
Program
Collaborative Health Research Projects
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
351063-2008
Name nom
Stashuk, Daniel
Department département
Systems Design Engineering
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2008
Competition year année de concours
2008
Award amount
71356
Program id
CHRPJ
Program nane en
Collaborative Health Research Projects
Program name fr
Projets de recherche concertée sur la santé
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
360
Committee name en
Collaborative Health Research Projects Selection Panel
Committee name fr
Comité de sélection de projets de recherche concertée sur la santé
Area of application code
1101
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Human health (including medically-related psychological research)
Area of application fr
Santé publique (y compris recherche psychologique liée à la médecine)
Research subject code
1901
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical technology
Research subject fr
Technologie biomédicale
Partie
1
Nb partie
2
Application title
Clinical characterization of muscle using quantitative electromyographic signal analysis
Keyword
NA
Application summary
Neuromuscular disorders change the structure and organization of the muscles involved and their complement of motor units (MUs). Characteristics of electromyographic (EMG) signals suitably detected from affected muscles can reflect the type and severity of disorder present. Proper interpretation of signal characteristics can assist with diagnosis, treatment and management. Currently, EMG signals are subjectively and qualitatively assessed for features consistent with nerve or muscle disease, chronicity, and severity. This assessment is subjective, and in part, highly dependent on the skill and experience of the examining physician and the extent to which progressive changes can be tracked is limited. Quantitative EMG (QEMG) analysis allows for a more objective, consistent and sensitive electrophysiological characterization. However, few applications capable of clinically acquiring QEMG data exist and more importantly a method for completing the crucial step of interpreting QEMG data to produce a characterization of the neuromuscular system is currently not available. The objectives of the proposed research are therefore to produce an integrated system for EMG signal acquisition, QEMG analysis and electrophysiological characterization for routine use in an EMG clinic. In addition, a comprehensive and intuitive rationale, and a valid measure of confidence will be provided for each characterization to support improved clinical decisions. Novel aspects of the new system will include: the integration of acquisition, analysis and interpretation; the ease and consistency of signal acquisition, the validity of the QEMG data provided; and the value of the electrophysiological characterization. Beyond facilitating the acquisition and quality of QEMG data, the main significance of this work will be improved interpretation of QEMG data which will contribute to better diagnosis, treatment and management of neuromuscular disorders. Furthermore, demonstrating an ability to readily complete these studies in an EMG clinic will increase the likelihood of the methods being translated to other clinical EMG machines for widespread impact.