Spasticity assessment based on the Hilbert–Huang transform marginal spectrum entropy and the root mean square of surface electromyography signals: a preliminary study

• Published in: Biomedical engineering online Vol. 17; no. 1; pp. 27 - 20
• Main Authors: Hu, Baohua, Zhang, Xiufeng, Mu, Jingsong, Wu, Ming, Wang, Yong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 27.02.2018; BioMed Central Ltd; BMC
• Subjects: Adult; Aged; Aged, 80 and over; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biotechnology; Electromyography; Engineering; Entropy; Female; HHT marginal spectrum entropy; Humans; Male; Middle Aged; Onset detection; Physiological aspects; RMS; sEMG; Signal Processing, Computer-Assisted; Spasticity; Spasticity assessment; Young Adult; China; Spasticity assessment; sEMG; RMS; Onset detection; HHT marginal spectrum entropy
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-018-0460-1

Background Most of the objective and quantitative methods proposed for spasticity measurement are not suitable for clinical application, and methods for surface electromyography (sEMG) signal processing are mainly limited to the time-domain. This study aims to quantify muscle activity in the time–frequency domain, and develop a practical clinical method for the objective and reliable evaluation of the spasticity based on the Hilbert–Huang transform marginal spectrum entropy (HMSEN) and the root mean square (RMS) of sEMG signals. Methods Twenty-six stroke patients with elbow flexor spasticity participated in the study. The subjects were tested at sitting position with the upper limb stretched towards the ground. The HMSEN of the sEMG signals obtained from the biceps brachii was employed to facilitate the stretch reflex onset (SRO) detection. Then, the difference between the RMS of a fixed-length sEMG signal obtained after the SRO and the RMS of a baseline sEMG signal, denoted as the RMS difference (RMSD), was employed to evaluate the spasticity level. The relations between Modified Ashworth Scale (MAS) scores and RMSD were investigated by Ordinal Logistic Regression (OLR). Goodness-of-fit of the OLR was obtained with Hosmer–Lemeshow test. Results The HMSEN based method can precisely detect the SRO, and the RMSD scores and the MAS scores were fairly well related (test: χ 2  = 8.8060, p  = 0.2669; retest: χ 2  = 1.9094, p  = 0.9647). The prediction accuracies were 85% (test) and 77% (retest) when using RMSD for predicting MAS scores. In addition, the test–retest reliability was high, with an interclass correlation coefficient of 0.914 and a standard error of measurement of 1.137. Bland–Altman plots also indicated a small bias. Conclusions The proposed method is manually operated and easy to use, and the HMSEN based method is robust in detecting SRO in clinical settings. Hence, the method is applicable to clinical practice. The RMSD can assess spasticity in a quantitative way and provide greater resolution of spasticity levels compared to the MAS in clinical settings. These results demonstrate that the proposed method could be clinically more useful for the accurate and reliable assessment of spasticity and may be an alternative clinical measure to the MAS.