Improving EMG Signal Change Point Detection for Low SNR by Using Extended Teager-Kaiser Energy Operator

Muscle onset detection plays a key role in applications ranging from clinical to assistive technology. The Teager-Kaiser energy operator (TKEO) is an acknowledged tool used in surface electromyography (sEMG) signal conditioning for improving the performances of many change-point detection methods. Here, a TKEO extended version (ETKEO) was used to investigate its effects, for different SNR ranges, among a series of well-assessed algorithms, including a threshold-based one (TP). An optimization procedure on synthetic signals for the selection of the operator structure was also developed. The detection errors between TKEO and ETKEO, performed on real sEMG signals with SNR≤8 dB, showed significant (
${p} < 0.05$
) overall improvements, not lower than 30%, when ETKEO was used. When compared with more robust techniques preconditioned by ETKEO as well, i.e., wavelet-, CUSUM- and profile likelihood maximization-based algorithms, the TP detector reached comparable performances for each SNR band, also for the lowest one. The results support the relevance of using ETKEO to improve onset analysis methods for a wide range of low SNR values, being particularly suitable for applications such as myoelectric motion intention detection. Moreover, the ETKEO adaptable structure suggests its use for other biological signals, presenting different characteristics with respect to sEMG signals.