Comparing the Accuracy of Visual and Computerized Onset Detection Methods on Simulated Electromyography Signals with Varying Signal-to-Noise Ratios

Erik Kowalski,Mario Lamontagne,Danilo S Catelli

doi:10.3390/jfmk6030070

Erik Kowalski, Mario Lamontagne + Show 1 more

Open Access

https://doi.org/10.3390/jfmk6030070

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Abstract

Electromyography (EMG) onsets determined by computerized detection methods have been compared against the onsets selected by experts through visual inspection. However, with this type of approach, the true onset remains unknown, making it impossible to determine if computerized detection methods are better than visual detection (VD) as they can only be as good as what the experts select. The use of simulated signals allows for all aspects of the signal to be precisely controlled, including the onset and the signal-to-noise ratio (SNR). This study compared three onset detection methods: approximated generalized likelihood ratio, double threshold (DT), and VD determined by eight trained individuals. The selected onset was compared against the true onset in simulated signals which varied in the SNR from 5 to 40 dB. For signals with 5 dB SNR, the VD method was significantly better, but for SNRs of 20 dB or greater, no differences existed between the VD and DT methods. The DT method is recommended as it can improve objectivity and reduce time of analysis when determining EMG onsets. Even for the best-quality signals (SNR of 40 dB), all the detection methods were off by 15–30 ms from the true onset and became progressively more inaccurate as the SNR decreased. Therefore, although all the detection methods provided similar results, they can be off by 50–80 ms from the true onset as the SNR decreases to 10 dB. Caution must be used when interpreting EMG onsets, especially on signals where the SNR is low or not reported at all.

Highlights

It is a valuable tool involved in the fields of biomechanics, motor control, neuromuscular physiology, postural control, and physical therapy [2]
Noise in EMG signals comes from a variety of sources, including inherent noise generated from electronic equipment, movement artifacts, electromagnetic noise, crosstalk from neighboring muscles, electrocardiographic artifacts, internal noise, or inherent instability of the EMG signal itself [25]
This study was successful in showing that the concept of an EMG onset training tool can improve a researcher’s visual detection (VD) of signal onset

Summary

Introduction

Electromyography (EMG) can be a valuable tool in measuring skeletal muscle electrical output during physical activities and provides easy access to the physiological processes that cause muscles to generate force in order to produce movement [1]. It is a valuable tool involved in the fields of biomechanics, motor control, neuromuscular physiology, postural control, and physical therapy [2]. The EMG signal is evaluated in the time domain and in the spectral domain. Mean and median frequencies are evaluated to provide insight into the muscle biochemistry, contraction characteristics [3,4], and muscle fatigue [5]. Common parameters used in the time domain include the root mean square, the average rectified value, and the peak linear envelope [1]

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