Spasticity Measurement Based on the HHT Marginal Spectrum Entropy of sEMG Using a Portable System: A Preliminary Study

Abstract

To facilitate stretch reflex onset (SRO) detection and improve accuracy and reliability of spasticity assessment in clinical settings, a new method to measure dynamic stretch reflex threshold (DSRT) based on Hilbert-Huang transform marginal spectrum entropy (HMSEN) of surface electromyography (sEMG) signals and a portable system to quantify modified Ashworth scale (MAS) for spasticity assessment were developed. The sEMG signals were divided into frames using a fixed-length sliding window, and the HMSEN of each frame was calculated. An adaptive threshold was set to measure the DSRT. The HMSEN based method can quantify muscle activity through time-frequency and nonlinear dynamics analysis, therefore providing deeper insight about the spastic muscle mechanisms during stretching and a reliable SRO detection method. Experimental results revealed that the HMSEN based method could reliably detect the SRO and measure the DSRT (recognition rate: 95.45%), and could achieve improved performance over the time-domain based method. There was a strong correlation ( to -0.900) between the MAS scores and the DSRT index, and the test-retest reliability was high. Additionally, limitations of the MAS were analyzed. This paper indicates that the presented framework can provide a promising tool to measure DSRT and a clinical quantitative approach for spasticity assessment.

Increased Muscle Activity Accompanying With Decreased Complexity as Spasticity Appears: High-Density EMG-Based Case Studies on Stroke Patients

“…In the three stroke patients, the activation area was different indicated that the spatial activation pattern of a spastic muscle might vary between individuals. A previous study showed that the MUs recruitment pattern resulted from the stretch reflex changed depending on the level of spasticity (Hu et al, 2018).…”

“…As a complexity indicator, a small entropy value is associated with small complexity and great regularity. Studies have shown that the complexity of EMG signals changes in stroke patients with spasticity during voluntary contraction (Dash et al, 2019), and the entropy-based method can reliably and precisely detect the stretch reflex onset (Hu et al, 2018).…”

A spasticity assessment method for voluntary movement using data fusion and machine learning

“…Although the mechanical signalbased approach has good interpretability, the absence of muscle activation analysis makes it difficult to identify the disturbance caused by contractures. In current studies, it is generally believed that TSRT [20], [21], [22] based on inertial measurement unit (IMU) and surface electromyography (sEMG) fusion exhibits reliable clinical validity. [23] shows that the neural component is the domination factor of spasticity assessment for most patients in the passive stretch test.…”

Detection of stretch reflex onset based on empirical mode decomposition and modified sample entropy

“…The stretch reflex threshold represents the joint angle at which antagonist muscles or motor neurons begin to contract when the affected limbs of the subject are stretched passively [6]. The starting point that corresponds to the beginning of contraction of antagonistic muscles or motor neurons is called stretch reflex onset [7]. Surface electromyogram (EMG) is a nonlinear and nonstationary signal obtained from electrodes attached on the skin surface of muscles [8].…”