Abstract
Objective. Accurate identification of surface electromyography (EMG) muscle onset is vital when examining short temporal parameters such as electromechanical delay. The visual method is considered the ‘gold standard’ in onset detection. Automatic detection methods are commonly employed to increase objectivity and reduce analysis time, but it is unclear if they are sensitive enough to accurately detect EMG onset when relating them to short-duration motor events. Approach. This study aimed to determine: (1) if automatic detection methods could be used interchangeably with visual methods in detecting EMG onsets (2) if the Teager–Kaiser energy operator (TKEO) as a conditioning step would improve the accuracy of popular EMG onset detection methods. The accuracy of three automatic onset detection methods: approximated generalized likelihood ratio (AGLR), TKEO, and threshold-based method were examined against the visual method. EMG signals from fast, explosive, and slow, ramped isometric plantarflexor contractions were evaluated using each technique. Main results. For fast, explosive contractions, the TKEO was the best-performing automatic detection method, with a low bias level (4.7 ± 5.6 ms) and excellent intraclass correlation coefficient (ICC) of 0.993, however with wide limits of agreement (LoA) (−6.2 to +15.7 ms). For slow, ramped contractions, the AGLR with TKEO conditioning was the best-performing automatic detection method with the smallest bias (11.3 ± 32.9 ms) and excellent ICC (0.983) but produced wide LoA (−53.2 to +75.8 ms). For visual detection, the inclusion of TKEO conditioning improved inter-rater and intra-rater reliability across contraction types compared with visual detection without TKEO conditioning. Significance. In conclusion, the examined automatic detection methods are not sensitive enough to be applied when relating EMG onset to a motor event of short duration. To attain the accuracy needed, visual detection is recommended. The inclusion of TKEO as a conditioning step before visual detection of EMG onsets is recommended to improve visual detection reliability.
Highlights
Surface electromyography (EMG) is a widely used measurement technique for determining muscle activity in biomechanics, biomedical, and areas of sports sciences (De Luca 1997)
The acceptability of approximated generalized likelihood ratio (AGLR), threshold-based method (TBM), and Teager–Kaiser energy operator (TKEO) as alternatives to visual EMG onset detection were examined across contractions of varying rates of amplitude increase
The accuracy of EMG onset detection with and without TKEO conditioning applied to the detection methods was examined
Summary
Surface electromyography (EMG) is a widely used measurement technique for determining muscle activity in biomechanics, biomedical, and areas of sports sciences (De Luca 1997). An important application of EMG is the precise detection of temporal characteristics of muscle recruitment, such as muscle activity onset and offset times. Temporal analysis of surface EMG data has been widely employed to quantify electromechanical delay (EMD), which is the delay between muscle activation and force production (Cavanagh and Komi 1979). Accurate identification of EMG onset is vital when examining temporal parameters such as EMD, where the delay period between EMG onset and force may be as low as 10 ms for voluntary contractions (Tillin et al 2010). EMG onset is one of the most commonly studied parameters in surface EMG analysis; methods of detection vary across the literature. A detection method with high validity and reliability would enable comparisons between muscles, participants, and experimental conditions across research studies
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