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JOURNAL
OF
SPORTS SCIENCE &
MEDICINE
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Research
article
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DETERMINATION OF AN OPTIMAL THRESHOLD VALUE FOR MUSCLE ACTIVITY DETECTION IN EMG ANALYSIS |
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Kerem Tuncay Özgünen1  ,
Umut Çelik2 and Sanlı Sadi Kurdak1 |
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Çukurova University, 1Faculty of Medicine, Department of Physiology, Division of Sports Physiology, 2Faculty of Engineering and Architecture, Department of Electrical and Electronics Engineering, Adana, Turkey. |
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© Journal of Sports Science and Medicine (2010) 9, 620 - 628 |
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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 |
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