Surface electromyography low-frequency content: Assessment in isometric conditions after electrocardiogram cancellation by the Segmented-Beat Modulation Method

• Main Authors: Agnese Sbrollini, Annachiara Strazza, Silvia Candelaresi, Ilaria Marcantoni, Micaela Morettini, Sandro Fioretti, Francesco Di Nardo, Laura Burattini
• Format: Article
• Language: English
• Published: Elsevier 2018-01-01
• Series: Informatics in Medicine Unlocked
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352914818301801

Background: Surface electromyography (SEMG) is widely used in clinics for assessing muscle functionality. All procedures proposed for noise reduction alter SEMG spectrum, especially in the low-frequency band (below 30 Hz). Indeed, low-frequency band is generally addressed to motion artifacts and electrocardiogram (ECG) interference without any further investigation on the possibility of SEMG having significant spectral content. The aim of the present study was evaluating SEMG frequency content to understand if low-frequency spectral content is negligible or, on the contrary, represents a significant SEMG portion potentially providing relevant clinical information. Method: Isometric recordings of five muscles (sternocleidomastoideus, erectores spinae at L4, rectus abdominis, rectus femoris and tibialis anterior) were acquired in 10 young healthy voluntary subjects. These recordings were not affected by motion artifacts by construction and were pre-processed by the Segmented-Beat Modulation Method for ECG deletion before performing spectral analysis. Results: Results indicated that SEMG frequency content is muscle and subject dependent. Overall, the 50th[25th;75th] percentiles spectrum median frequency and spectral power below 30 Hz were 74[54; 87] Hz and 18[10; 31] % of total (0–450 Hz) spectral power. Conclusions: Low-frequency spectral content represents a significant SEMG portion and should not be neglected. Keywords: Surface electromyographic signal, Electromyographic spectrum, Segmented-Beat Modulation Method, Non-linear filtering, Spectral analysis