Ping Zhou

Examination of Poststroke Alteration in Motor Unit Firing Behavior Using High-Density Surface EMG Decomposition

Recent advances in high-density surface electromyogram (EMG) decomposition have made it a feasible task to discriminate single motor unit activity from surface EMG interference patterns, thus providing a noninvasive approach for examination of motor unit control properties. In the current study, we applied high-density surface EMG recording and decomposition techniques to assess motor unit firing behavior alterations poststroke. Surface EMG signals were collected using a 64-channel 2-D electrode array from the paretic and contralateral first dorsal interosseous (FDI) muscles of nine hemiparetic stroke subjects at different isometric discrete contraction levels between 2 to 10 N with a 2 N increment step. Motor unit firing rates were extracted through decomposition of the high-density surface EMG signals and compared between paretic and contralateral muscles. Across the nine tested subjects, paretic FDI muscles showed decreased motor unit firing rates compared with contralateral muscles at different contraction levels. Regression analysis indicated a linear relation between the mean motor unit firing rate and the muscle contraction level for both paretic and contralateral muscles (p <; 0.001), with the former demonstrating a lower increment rate (0.32 pulses per second (pps)/N) compared with the latter (0.67 pps/N). The coefficient of variation (averaged over the contraction levels) of the motor unit firing rates for the paretic muscles (0.21 ± 0.012) was significantly higher than for the contralateral muscles (0.17 ± 0.014) (p <; 0.05). This study provides direct evidence of motor unit firing behavior alterations poststroke using surface EMG, which can be an important factor contributing to hemiparetic muscle weakness.

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

This study presents a progressive FastICA peel-off (PFP) framework for high-density surface electromyogram (EMG) decomposition. The novel framework is based on a shift-invariant model for describing surface EMG. The decomposition process can be viewed as progressively expanding the set of motor unit spike trains, which is primarily based on FastICA. To overcome the local convergence of FastICA, a peel-off strategy, i.e., removal of the estimated motor unit action potential trains from the previous step, is used to mitigate the effects of the already identified motor units, so more motor units can be extracted. A constrained FastICA is applied to assess the extracted spike trains and correct possible erroneous or missed spikes. These procedures work together to improve decomposition performance. The proposed framework was validated using simulated surface EMG signals with different motor unit numbers (30, 70, 91) and SNRs (20, 10, and 0 dB). The results demonstrated relatively large numbers of extracted motor units and high accuracies (high F1-scores). The framework was tested with 111 trials of 64-channel electrode array experimental surface EMG signals during the first dorsal interosseous muscle contraction at different intensities. On average 14.1 ±5.0 motor units were identified from each trial of experimental surface EMG signals.

Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings.

There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3D IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their MU action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings.

EMG feature assessment for myoelectric pattern recognition and channel selection: A study with incomplete spinal cord injury

Myoelectric pattern recognition with a large number of electromyogram (EMG) channels provides an approach to assessing motor control information available from the recorded muscles. In order to develop a practical myoelectric control system, a feature dependent channel reduction method was developed in this study to determine a small number of EMG channels for myoelectric pattern recognition analysis. The method selects appropriate raw EMG features for classification of different movements, using the minimum Redundancy Maximum Relevance (mRMR) and the Markov random field (MRF) methods to rank a large number of EMG features, respectively. A k-nearest neighbor (KNN) classifier was used to evaluate the performance of the selected features in terms of classification accuracy. The method was tested using 57 channels surface EMG signals recorded from forearm and hand muscles of individuals with incomplete spinal cord injury (SCI). Our results demonstrate that appropriate selection of a small number of raw EMG features from different recording channels resulted in similar high classification accuracies as achieved by using all the EMG channels or features. Compared with the conventional sequential forward selection (SFS) method, the feature dependent method does not require repeated classifier implementation. It can effectively reduce redundant information not only cross different channels, but also cross different features in the same channel. Such hybrid feature-channel selection from a large number of EMG recording channels can reduce computational cost for implementation of a myoelectric pattern recognition based control system.

Robust muscle activity onset detection using an unsupervised electromyogram learning framework.

Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscle activity onsets. The distribution of the logarithmic power of EMG signal was characterized by a two-component GMM in each frequency band, in which the two components respectively correspond to the posterior distribution of EMG burst and non-burst logarithmic powers. The parameter set of the GMM was sequentially estimated based on maximum likelihood, subject to constraints derived from the relationship between EMG burst and non-burst distributions. An optimal threshold for EMG burst/non-burst classification was determined using the GMM at each frequency band, and the final decision was obtained by a voting procedure. The proposed novel framework was applied to simulated and experimental surface EMG signals for muscle activity onset detection. Compared with conventional approaches, it demonstrated robust performance for low and changing signal to noise ratios in a dynamic environment. The framework is applicable for real-time implementation, and does not require the assumption of non EMG burst in the initial stage. Such features facilitate its practical application.

Activation deficit correlates with weakness in chronic stroke: Evidence from evoked and voluntary EMG recordings

OBJECTIVEnTo use evoked (M-wave) and voluntary (during maximal voluntary contraction (MVC)) EMG recordings to estimate the voluntary activation level in chronic stroke.nnnMETHODSnNine chronic hemiparetic stroke subjects participated in the experiment. M-wave (EMGM-wave) and MVC (EMGMVC) EMG values of the biceps brachii muscles were recorded.nnnRESULTSnPeak torque was significantly smaller on the impaired than non-impaired side. EMGM-wave was also significantly smaller on the impaired than non-impaired side. However, the normalized EMGM-wave/TorqueMVC ratio was not significantly different between two sides. In contrast, both absolute EMGMVC and normalized EMGMVC/TorqueMVC were smaller on the impaired than non-impaired side. The voluntary activation level, EMGMVC/M-wave, was also smaller on the impaired than non-impaired side. The voluntary activation level on the impaired side was highly correlated with weakness (R=0.72), but very low (R=0.32) on the non-impaired side.nnnCONCLUSIONnCollectively, our findings suggest that both peripheral and central factors contribute to post-stroke weakness, but activation deficit correlates most closely with weakness as estimated from maximum voluntary torque generation.nnnSIGNIFICANCEnThese findings serve to highlight the potential benefit from high-intensity exercises to enhance central activation for facilitation of motor recovery.

Correlation of Resting Elbow Angle with Spasticity in Chronic Stroke Survivors

Objective To evaluate whether resting joint angle is indicative of severity of spasticity of the elbow flexors in chronic stroke survivors. Methods Seventeen hemiparetic stroke subjects (male: nu2009=u200913; female: nu2009=u20094; age: 37–89u2009years; 11 right and 6 left hemiplegia; averaged 54.8u2009months after stroke, ranging 12–107u2009months) participated in the study. The number of subjects with modified Ashworth scale score (MAS)u2009=u20090, 1, 1+, 2, and 3 was 3, 3, 5, 3, and 3, respectively. In a single experimental session, resting elbow joint angle, MAS, and Tardieu scale score (Tardieu R1) were measured. A customized motorized stretching device was used to stretch elbow flexors at 5, 50, and 100°/s, respectively. Biomechanical responses (peak reflex torque and reflex stiffness) of elbow flexors were quantified. Correlation analyses between clinical and biomechanical assessments were performed. Results Resting elbow joint angle showed a strong positive correlation with Tardieu R1 (ru2009=u20090.77, pu2009<u20090.01) and a very strong negative correlation with MAS (ru2009=u2009−0.89, pu2009<u20090.01). The resting angle also had strong correlations with biomechanical measures (ru2009=u2009−0.63 to −0.76, pu2009<u20090.01). Conclusion Our study provides experimental evidence for anecdotal observation that the resting elbow joint angle correlates with severity of spasticity in chronic stroke. Resting angle observation for spasticity assessment can and will be an easy, yet a valid way of spasticity estimation in clinical settings, particularly for small muscles or muscles which are not easily measurable by common clinical methods.

Analysis of linear electrode array EMG for assessment of hemiparetic biceps brachii muscles

This study presents a frequency analysis of surface electromyogram (EMG) signals acquired by a linear electrode array from the biceps brachii muscles bilaterally in 14 hemiparetic stroke subjects. For different levels of isometric contraction ranging from 10 to 80% of the maximum voluntary contraction (MVC), the power spectra of 19 bipolar surface EMG channels arranged proximally to distally along the muscle fibers were examined in both paretic and contralateral muscles. It was found that across all stroke subjects, the median frequency (MF) and the mean power frequency (MPF), averaged from different surface EMG channels, were significantly smaller in the paretic muscle compared to the contralateral muscle at each of the matched percent MVC contractions. The muscle fiber conduction velocity (MFCV) was significantly slower in the paretic muscle than in the contralateral muscle. No significant correlation between the averaged MF, MPF, or MFCV vs. torque was found in both paretic and contralateral muscles. However, there was a significant positive correlation between the global MFCV and MF. Examination of individual EMG channels showed that electrodes closest to the estimated muscle innervation zones produced surface EMG signals with significantly higher MF and MPF than more proximal or distal locations in both paretic and contralateral sides. These findings suggest complex central and peripheral neuromuscular alterations (such as selective loss of large motor units, disordered control of motor units, increased motor unit synchronization, and atrophy of muscle fibers, etc.) which can collectively influence the surface EMG signals. The frequency difference with regard to the innervation zone also confirms the relevance of electrode position in surface EMG analysis.

Examination of Hand Muscle Activation and Motor Unit Indices Derived from Surface EMG in Chronic Stroke

In this study, we used muscle and motor unit indices, derived from convenient surface electromyography (EMG) measurements, for examination of paretic muscle changes post stroke. For 12 stroke subjects, compound muscle action potential and voluntary surface EMG signals were recorded from paretic and contralateral first dorsal interosseous, abductor pollicis brevis, and abductor digiti minimi muscles. Muscle activation index (AI), motor unit number index (MUNIX), and motor unit size index (MUSIX) were then calculated for each muscle. There was a significant AI reduction for all the three muscles in paretic side compared with contralateral side, providing an evidence of muscle activation deficiency after stroke. The hand MUNIX (defined by summing the values from the three muscles) was significantly reduced in paretic side compared with contralateral side, whereas the hand MUSIX was not significantly different. Furthermore, diverse changes in MUNIX and MUSIX were observed from the three muscles. A major feature of the present examinations is the primary reliance on surface EMG, which offers practical benefits because it is noninvasive, induces minimal discomfort and can be performed quickly.

Complexity Analysis of Surface EMG for Overcoming ECG Interference toward Proportional Myoelectric Control

Electromyographic (EMG) signals from muscles in the body torso are often contaminated by electrocardiography (ECG) interferences, which consequently compromise EMG intensity estimation. The ECG interference has become a barrier to proportional control of myoelectric prosthesis using a neural machine interface called targeted muscle reinnervation (TMR), which involves transferring the residual amputated nerves to nonfunctional muscles (typically pectoralis muscles for high level amputations). This study investigates a novel approach toward implementation of proportional myoelectric control by applying sample entropy (SampEn) analysis of surface EMG signals for robust intensity estimation in the presence of significant ECG interference. Surface EMG data from able-bodied and TMR amputee subjects with different degrees of ECG interference were used for performance evaluation. The results showed that the SampEn analysis had high correlation with surface EMG amplitude measurement but low sensitivity to different degrees of ECG interference. Taking this advantage, SampEn analysis of surface EMG signal can be used to facilitate implementation of proportional myoelectric control against ECG interference. This is particularly important for TMR prosthesis users.