Memorial Hermann Research Center

Houston, TX, United States

Memorial Hermann Research Center

Houston, TX, United States
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Chen M.,Hefei University of Technology | Chen M.,Guangdong Work Injury Rehabilitation Center | Holobar A.,University of Maribor | Zhang X.,Hefei University of Technology | And 3 more authors.
Neural Plasticity | Year: 2016

Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6 ± 4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85 ± 1.85 % in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield. © 2016 Maoqi Chen et al.

Li L.,Sun Yat Sen University | Li L.,University of Texas Health Science Center at Houston | Li L.,Memorial Hermann Research Center | Li X.,University of Texas Health Science Center at Houston | And 9 more authors.
PLoS ONE | Year: 2016

This study investigates the impact of the subcutaneous fat layer (SFL) thickness on localized electrical impedance myography (EIM), as well as the effects of different current electrodes, varying in distance and direction, on EIM output. Twenty-three healthy subjects underwent localized multi-frequency EIM on their biceps brachii muscles with a hand-held electrode array. The EIM measurements were recorded under three different configurations: wide (or outer) longitudinal configuration 6.8 cm, narrow (or inner) longitudinal configuration 4.5 cm, and narrow transverse configuration 4.5 cm. Ultrasound was applied to measure the SFL thickness. Coefficients of determination (R2) of three EIM variables (resistance, reactance, and phase) and SFL thickness were calculated. For the longitudinal configuration, the wide distance could reduce the effects of the subcutaneous fat when compared with the narrow distance, but a significant correlation still remained for all three EIM parameters. However, there was no significant correlation between SFL thickness and reactance in the transverse configuration (R2 = 0.0294, p = 0.434). Utilizing a ratio of 50kHz/100kHz phase was found to be able to help reduce the correlation with SFL thickness for all the three configurations. The findings indicate that the appropriate selection of the current electrode distance, direction and the multi-frequency phase ratio can reduce the impact of subcutaneous fat on EIM. These settings should be evaluated for future clinical studies using hand-held localized arrays to perform EIM. © 2016 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Wang D.,University of Electronic Science and Technology of China | Zhang X.,University of Electronic Science and Technology of China | Chen X.,University of Electronic Science and Technology of China | Zhou P.,Hefei University of Technology | And 2 more authors.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 | Year: 2014

Myoelectric pattern recognition applied to high-density surface electromyographic (sEMG) recordings from paretic muscles has been proven to identify various movement intents of stroke survivors, thus facilitating the design of myoelectrically controlled robotic systems for recovery of upper-limb dexterity. Aiming at effectively decoding neural control information under the condition of neurological injury following stroke, this paper further investigates the application of wavelet packet transform (WPT) on myoelectric feature extraction to identify 20 functional movements performed by the paretic upper limb of 4 chronic stroke subjects. The WPT was used to decompose the original sEMG signals via a tree of subspaces, where optimal ones were selected in term of the classification efficacy. The energies in the selected subspaces were calculated as optimal wavelet packet features, which were finally fed into a linear discriminant classifier. The WPT-based myoelectric feature extraction approach achieved accuracies above 94% for all subjects in a user-specific condition, demonstrating its potential applications in upper limb rehabilitation after stroke. © 2014 IEEE.

Liu Y.,University of Houston | Ning Y.,University of Houston | Li S.,University of Texas Health Science Center at Houston | Li S.,Memorial Hermann Research Center | And 5 more authors.
International Journal of Neural Systems | Year: 2015

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. © 2015 World Scientific Publishing Company.

Li X.,University of Texas Health Science Center at Houston | Li X.,Memorial Hermann Research Center | Nandedkar S.D.,Natus Medical | Zhou P.,University of Texas Health Science Center at Houston | And 2 more authors.
Medical Engineering and Physics | Year: 2016

The motor unit number index (MUNIX) technique has provided a quick and convenient approach to estimating motor unit population changes in a muscle. Reduction in motor unit action potential (MUAP) amplitude can lead to underestimation of motor unit numbers using the standard MUNIX technique. This study aims to overcome this limitation by developing a modified MUNIX (mMUNIX) technique. The mMUNIX uses a variable that is associated with the area of compound muscle action potential (CMAP) rather than an arbitrary fixed value (20 mV ms) as used in the standard MUNIX to define the output. The performance of the mMUNIX was evaluated using motoneuron pool and surface electromyography (EMG) models. With a fixed motor unit number, the mMUNIX output remained relatively constant with varying degrees of MUAP amplitude changes, while the standard MUNIX substantially underestimated the motor unit number in such cases. However, when MUAP amplitude remained unchanged, the mMUNIX showed less sensitivity than the standard MUNIX in tracking motor unit loss. The current simulation study demonstrated both the advantages and limitations of the standard and modified MUNIX techniques, which can help guide appropriate application and interpretation of MUNIX measurements. © 2015 IPEM.

Zhang X.,Anhui University of Science and Technology | Zhou P.,Anhui University of Science and Technology | Zhou P.,University of Texas Health Science Center at Houston | Zhou P.,Memorial Hermann Research Center
Journal of Healthcare Engineering | Year: 2014

This study presents a novel feature extraction method for myoelectric pattern recognition using a multivariate extension of empirical mode decomposition (EMD), namely multivariate EMD (MEMD). The method processes multiple surface electromyogram (EMG) channels simultaneously rather than in a channel-by-channel manner. From mode-aligned intrinsic mode functions (IMFs, representing signal components over multiple scales) derived from the MEMD analysis, normalized amplitude distributions of the same-mode/scale IMFs across different channels were calculated as features, which serve to reveal the underlying relationship in the aligned intrinsic scales across multiple muscles. The proposed method was assessed for identification of 18 different functional movement patterns via 27-channel surface EMG signals recorded from the paretic forearm muscles of 12 subjects with hemiparetic stroke. With a linear discriminant classifier, the proposed MEMD based feature set resulted in an average error rate of 4.61 ± 4.70% for classification of all the different movements, significantly lower than that of the conventional time-domain feature set (7.14 ± 6.15%, p < 0.05). The results indicate that the MEMD based feature extraction of multi-channel surface EMG data provides a promising approach to modeling of muscle couplings and identification of different myoelectric patterns. © 2014 Journal of Healthcare Engineering. All Rights Reserved.

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