Journal of Sports Science and Medicine
Journal of Sports Science and Medicine
ISSN: 1303 - 2968   
Ios-APP Journal of Sports Science and Medicine
Androit-APP Journal of Sports Science and Medicine
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©Journal of Sports Science and Medicine ( 2010 ) 09 , 620 - 628

Research article
Determination of an Optimal Threshold Value for Muscle Activity Detection in EMG Analysis
Kerem Tuncay Özgünen1, , Umut Çelik2, Sanlı Sadi Kurdak1
Author Information
1 Faculty of Medicine, Department of Physiology, Division of Sports Physiology, Çukurova University, Adana, Turkey
2 Faculty of Engineering and Architecture, Department of Electrical and Electronics Engineering, Çukurova University, Adana, Turkey

Kerem Tuncay Özgünen
✉ Çukurova University, Faculty of Medicine, Department of Physiology, Division of Sports Physiology, 01330 Balcalı, Adana, Turkey
Email: kozgunen@cu.edu.tr
Publish Date
Received: 22-06-2010
Accepted: 21-9-2010
Published (online): 01-12-2010
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ABSTRACT

It is commonly agreed that one needs to use a threshold value in the detection of muscle activity timing in electromyographic (EMG) signal analysis. However, the algorithm for threshold determination lacks an agreement between the investigators. In this study we aimed to determine a proper threshold value in an incremental cycling exercise for accurate EMG signal analysis. Nine healthy recreationally active male subjects cycled until exhaustion. EMG recordings were performed on four low extremity muscle groups; gastrocnemius lateralis (GL), gastrocnemius medialis (GM), soleus (SOL) and vastus medialis (VM). We have analyzed our data using three different threshold levels: 25%, 35% and 45% of the mean RMS EMG value. We compared the appropriateness of these threshold values using two criteria: (1) significant correlation between the actual and estimated number of bursts and (2) proximity of the regression line of the actual and estimated number of bursts to the line of identity. It had been possible to find a significant correlation between the actual and estimated number of bursts with the 25, 35 and 45% threshold values for the GL muscle. Correlation analyses for the VM muscle had shown that the number of bursts estimated with the 35% threshold value was found to be significantly correlated with the actual number of bursts. For the GM muscle, it had been possible to predict the burst number by using either the 35% or 45% threshold value and for the SOL muscle the 25% threshold value was found as the best predictor for actual number of burst estimation. Detailed analyses of the actual and estimated number of bursts had shown that success of threshold estimation may differ among muscle groups. Evaluation of our data had clearly shown that it is important to select proper threshold values for correct EMG signal analyses. Using a single threshold value for different exercise intensities and different muscle groups may cause misleading results.

Key words: Electromyography, cycling, incremental exercise, burst detection, threshold


           Key Points
  • accepted threshold value may cause erroneous results in EMG analysis.
  • Using a single threshold value for different exercise intensities and different muscle groups may cause misleading results.
  • The investigators may need to use different threshold selection strategies for different workloads.
  • The investigators have to justify the choice of threshold selection with valid arguments before detailed EMG signal analyses.
 
 
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