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

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.

Enhanced Dynamic Surface EMG Decomposition Using the Non-Negative Matrix Factorization and Three-Dimensional Motor Unit Localization

OBJECTIVE

Surface electromyography (sEMG) signal decomposition is of great importance in examining neuromuscular diseases and neuromuscular research, especially dynamic sEMG decomposition is even more technically challenging.

METHODS

A novel two-step sEMG decomposition approach was developed. The linear minimum mean square error estimation was first employed to extract estimated firing trains (EFTs) from the eigenvector matrices constructed using the non-negative matrix factorization (NMF). The firing instants of each EFT were then classified into motor units (MUs) according to their specific three-dimensional (3D) space position. The performance of the proposed approach was evaluated using simulated and experimentally recorded sEMG.

RESULTS

The simulation results demonstrated that the proposed approach can reconstruct MUAPTs with true positive rates of 89.12 ± 2.71%, 94.34 ± 1.85% and 95.45 ± 2.11% at signal-to-noise ratios of 10, 20, and 30 dB, respectively. The experimental results also demonstrated a high decomposition accuracy of 90.13 ± 1.31% in the two-source evaluation, and a high accuracy of 91.86 ± 1.14% in decompose-synthesize-decompose- compare evaluation.

CONCLUSIONS

The adoption of NMF reduces the dimension of random pattern under the restriction of non-negativity, as well as keeps the information unchanged as much as possible. The 3D space information of MUs enhances the classification accuracy by tackling the issue of relative movements between MUs and electrodes during dynamic contractions. The accuracy achieved in this study demonstrates the good performance and reliability of the proposed decomposition algorithm in dynamic surface EMG decomposition.

SIGNIFICANCE

The spatiotemporal information is applied to the dynamic surface EMG decomposition.

Motor unit distribution and recruitment in spastic and non-spastic bilateral biceps brachii muscles of chronic stroke survivors

Objective.This study aims to characterize the motor units (MUs) distribution and recruitment pattern in the spastic and non-spastic bilateral biceps brachii muscles (BBMs) of chronic stroke survivors.Approach.High-density surface electromyography (HD-sEMG) signals were collected from both spastic and non-spastic BBMs of fourteen chronic stroke subjects during isometric elbow flexion at 10%, 30%, 50% and 100% maximal voluntary contractions (MVCs). By combining HD-sEMG decomposition and bioelectrical source imaging, MU innervation zones (MUIZs) of the decomposed MUs were first localized in the 3D space of spastic and non-spastic BBMs. The MU depth defined as the distance between the localized MUIZ and its normal projection on the skin surface was then normalized to the arm radius of each subject and averaged at given contraction level. The averaged MU depth at different contraction levels on a specific arm side (intra-side) and the bilateral depths under a specific contraction level (inter-side) were compared.Main results.The average depth of decomposed MUs increased with the contraction force and significant differences observed between 10% vs 50% (p< 0.0001), 10% vs 100% (p< 0.0001) and 30% vs 100% MVC (p= 0.0017) on the non-spastic side, indicating that larger MUs with higher recruitment threshold locate in deeper muscle regions. In contrast, no force-related difference in MU depth was observed on the spastic side, suggesting a disruption of orderly recruitment of MUs with increase of force level, or the MU denervation and the subsequent collateral reinnervation secondary to upper motor neuron lesions. Inter-side comparison demonstrated significant MU depth difference at 10% (p= 0.0048) and 100% force effort (p= 0.0026).Significance.This study represents the first effort to non-invasively characterize the MU distribution inside spastic and non-spastic bilateral BBM of chronic stroke patients by combining HD-sEMG recording, EMG signal decomposition and bioelectrical source imaging. The findings of this study advances our understanding regarding the neurophysiology of human muscles and the neuromuscular alterations following stroke. It may also offer important MU depth information for botulinum toxin injection in clinical post-stroke spasticity management.

Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study

Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of this study was to evaluate how surface-EMG-based IZ estimation might be affected by different factors, including varying degrees of motor unit (MU) synchronization in the case of single or double IZs. The study was performed by implementing a model simulating surface EMG activity. Three different MU synchronization conditions were simulated, namely no synchronization, medium level synchronization, and complete synchronization analog to M wave. Surface EMG signals recorded by a 2-dimensional electrode array were simulated from a muscle with single and double IZs, respectively. For each situation, the IZ was estimated from surface EMG and compared with the one used in the model for performance evaluation. For the muscle with only one IZ, the estimated IZ location from surface EMG was consistent with the one used in the model for all the three MU synchronization conditions. For the muscle with double IZs, at least one IZ was appropriately estimated from interference surface EMG when there was no MU synchronization. However, the estimated IZ was different from either of the two IZ locations used in the model for the other two MU synchronization conditions. For muscles with a single IZ, MU synchronization has little effect on IZ estimation from electrode array surface EMG. However, caution is required for multiple IZ muscles since MU synchronization might lead to false IZ estimation.

Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.

Improving Botulinum Toxin Efficiency in Treating Post-Stroke Spasticity Using 3D Innervation Zone Imaging

Spasticity is a common post-stroke syndrome that imposes significant adverse impacts on patients and caregivers. This study aims to improve the efficiency of botulinum toxin (BoNT) in managing spasticity, by utilizing a three-dimensional innervation zone imaging (3DIZI) technique based on high-density surface electromyography (HD-sEMG) recordings. Stroke subjects were randomly assigned to two groups: the control group ([Formula: see text]) which received standard ultrasound-guided injections, and the experimental group ([Formula: see text]) which received 3DIZI-guided injections. The amount of BoNT given was consistent for all subjects. The Modified Ashworth Scale (MAS), compound muscle action potential (CMAP) and muscle activation volume (MAV) from bilateral biceps brachii muscles were obtained at the baseline, 3 weeks, and 3 months after injection. Intra-group and inter-group comparisons of MAS, CMAP amplitude and MAV were performed. An overall improvement in MAS of spastic elbow flexors was observed during the 3-week visit ([Formula: see text]), yet no statistically significant difference found with intra-group or inter-group analysis. Compared to the baseline, a significant reduction of CMAP amplitude and MAV were observed in the spastic biceps muscles of both groups at 3-week post-injection, and returned to approximate baseline value at 12-week post injection. A significantly higher reduction was found in CMAP amplitude ([Formula: see text]% versus [Formula: see text]%, [Formula: see text]) and MAV ([Formula: see text]% versus [Formula: see text]%, [Formula: see text]) in the experimental group compared to the control group. The study has demonstrated preliminary evidence that precisely directing BoNT to the innervation zones (IZs) localized by 3DIZI leads to a significantly higher treatment efficiency improvement in spasticity management. Results have also shown the feasibility of developing a personalized BoNT injection technique for the optimization of clinical treatment for post-stroke spasticity using proposed 3DIZI technique.

Global Innervation Zone Identification With High-Density Surface Electromyography

OBJECTIVE

The aim of this study is to compare the performance of three strategies in determining the global innervation zone (IZ) distribution.

METHODS

High-density surface electromyography was recorded from the biceps brachii muscle of seven healthy subjects under isometric voluntary contractions at 20%, 50%, and 100% of the maximal voluntary contraction and supramaximal musculocutaneous nerve stimulations. IZs were detected: first, by visual identification in a column-specific manner (IZ-1D); second, based on decomposed bipolar mapping of motor unit action potentials (IZ-2D); and third, by source imaging in the three-dimensional muscle space (IZ-3D).

RESULTS

All three IZ detection approaches have exhibited excellent trial-to-trial repeatability. Consistent IZ results were found in the axial direction of the arm across all three approaches, yet a difference was observed in the mediolateral direction.

CONCLUSIONS

Among all three approaches, IZ-3D is capable of providing the most comprehensive information regarding the global IZ distribution, while maintaining high consistency with IZ-1D and IZ-2D results.

SIGNIFICANCE

IZ-3D approach can be a potential tool for global IZ imaging, which is critical to the clinical diagnosis and treatment of neuromuscular disorders.

Three dimensional innervation zone imaging in spastic muscles of stroke survivors

OBJECTIVE

The outcome of botulinum toxin (BTX) therapy of post-stroke spasticity relies largely on accuracy of BTX injection to the proximity of innervation zones (IZs). Recently developed three-dimensional IZ imaging (3DIZI) is the only technique currently available to provide 3D distributions of IZs in vivo, yet its performance has not been validated under pathological conditions.

APPROACH

The performance of 3DIZI was evaluated in the spastic biceps brachii muscles of four chronic stroke subjects. High-density surface electromyography (sEMG) and intramuscular electromyography (iEMG) were simultaneously recorded. The IZ location in the 3D space of the spastic biceps calculated using the 3DIZI technique from sEMG recordings were compared against the IZ location detected using intramuscular wires.

MAIN RESULTS

3DIZI successfully reconstructed the IZs in the 3D space of the spastic biceps of all four stroke subjects, with a localization error of 4.7  ±  2.7 mm, and specifically a depth error of 1.8  ±  0.4 mm.

SIGNIFICANCE

Results have demonstrated the robust performance of 3DIZI under pathological conditions, laying a solid foundation for clinical application of 3D source imaging in leading precise BTX injections for spasticity management.

Motor unit innervation zone localization based on robust linear regression analysis

With the aim of developing a flexible and reliable procedure for superficial muscle innervation zone (IZ) localization, we proposed a method to estimate IZ location using surface electromyogram (EMG) based on robust linear regression. Regression lines were used to model the bidirectional propagation pattern of a single motor unit action potential (MUAP) and visualize the trajectory of the MUAP propagation. IZ localization was performed by identifying the origin of the bidirectional MUAP propagation. Robust linear regression and MUAP peak detection, combined with propagation phase reversal identification, may provide an efficient way to estimate IZ location. Our method offers high resolution in locating IZs based on simulation studies and experimental tests. Furthermore, our method is flexible and may also be applied using a relatively small number of EMG channels. A comparative study of the proposed method with the cross-correlation method for IZ localization was conducted. The results obtained with simulated MUAPs and measured spontaneous MUAPs in the biceps brachii muscle in six subjects (four males and two females, 57 ± 10 years old) with amyotrophic lateral sclerosis (ALS). Our method achieved estimation performance comparable to that obtained by using the cross-correlation method but with higher resolution. This study provides an accurate and practical method to estimate IZ location.

Reconstruction of the Human Hand Functional Structure Based On a Magnetomyogram

The new method of magnetomyography data analysis is proposed. The method is based on the Fourier transform of prolonged time series and on the massive solution of the inverse problem for all spectral components. For the method testing the following experiment was proposed. The subject clenched and relaxed the hand for five minutes, holding the handle, fixed on the table. Magnetomyograms were registered near the hand using the 7-channel SQUID-magnetometer based on the axial second-order gradiometers. The subject and experimental setup were placed inside a thick-walled aluminum camera, designed for shielding from an alternating electromagnetic field. No shielding from static magnetic field was used. Magnetomyograms with amplitude 20 picoTesla were registered in broad frequency band (up to 500 Hz), signal to noise ratio was more than 20. After filtering and extracting of clench/relax periods two synthetic 135 seconds myograms were formed. The multichannel spectra were calculated, and the functional tomograms were estimated. In case of the relaxed hand, no significant object was reconstructed. In case of the clenched hand, the 3D-object was extracted, representing the functional structure of the muscles, tensed in this experiment. The method can be used for diagnostics and study of the human muscle system.

Two-Source Validation of Progressive FastICA Peel-Off for Automatic Surface EMG Decomposition in Human First Dorsal Interosseous Muscle

This study aims to assess the accuracy of a novel high density surface electromyogram (SEMG) decomposition method, namely automatic progressive FastICA peel-off (APFP), for automatic decomposition of experimental electrode array SEMG signals. A two-source method was performed by simultaneous concentric needle EMG and electrode array SEMG recordings from the human first dorsal interosseous (FDI) muscle, using a protocol commonly applied in clinical EMG examination. The electrode array SEMG was automatically decomposed by the APFP while the motor unit action potential (MUAP) trains were also independently identified from the concentric needle EMG. The degree of agreement of the common motor unit (MU) discharge timings decomposed from the two different categories of EMG signals was assessed. A total of 861 and 217 MUs were identified from the 114 trials of simultaneous high density SEMG and concentric needle EMG recordings, respectively. Among them 168 common (MUs) were found with a high average matching rate of [Formula: see text] for the discharge timings. The outcomes of this study show that the APFP can reliably decompose at least a subset of MUs in the high density SEMG signals recorded from the human FDI muscle during low contraction levels using a protocol analog to clinical EMG examination.

A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings

To investigate the spatial information of individual motor unit (MUs) using multi-channel surface electromyography (EMG) decomposition. The K-means clustering convolution kernel compensation (KmCKC) approach was employed to detect the innervation pulse trains (IPTs) from the simulated surface EMG signals, and the motor unit action potentials (MUAPs) were evaluated using the spike-triggered average (STA) technique. The relationships between the features of MUAP and MU depth were determinated with a least square fitting method. The errors of peak-to-peak (PTP) amplitude of reconstructed MUAPs were less than 5.73%, even with 0 dB signal-to-noise (SNR). The fitting errors with nonlinear model were less than 5.55% for SNRs higher than 20 dB. The results show that it is possible to provide a useful method for estimating MU depth from surface EMG recordings. It is expected to extend the applicability of surface EMG technique to more challenging clinical applications.

Modern Theories of Pelvic Floor Support : A Topical Review of Modern Studies on Structural and Functional Pelvic Floor Support from Medical Imaging, Computational Modeling, and Electromyographic Perspectives

PURPOSE OF REVIEW

Weakened pelvic floor support is believed to be the main cause of various pelvic floor disorders. Modern theories of pelvic floor support stress on the structural and functional integrity of multiple structures and their interplay to maintain normal pelvic floor functions. Connective tissues provide passive pelvic floor support while pelvic floor muscles provide active support through voluntary contraction. Advanced modern medical technologies allow us to comprehensively and thoroughly evaluate the interaction of supporting structures and assess both active and passive support functions. The pathophysiology of various pelvic floor disorders associated with pelvic floor weakness is now under scrutiny from the combination of (1) morphological, (2) dynamic (through computational modeling), and (3) neurophysiological perspectives. This topical review aims to update newly emerged studies assessing pelvic floor support function among these three categories.

RECENT FINDINGS

A literature search was performed with emphasis on (1) medical imaging studies that assess pelvic floor muscle architecture, (2) subject-specific computational modeling studies that address new topics such as modeling muscle contractions, and (3) pelvic floor neurophysiology studies that report novel devices or findings such as high-density surface electromyography techniques. We found that recent computational modeling studies are featured with more realistic soft tissue constitutive models (e.g., active muscle contraction) as well as an increasing interest in simulating surgical interventions (e.g., artificial sphincter). Diffusion tensor imaging provides a useful non-invasive tool to characterize pelvic floor muscles at the microstructural level, which can be potentially used to improve the accuracy of the simulation of muscle contraction. Studies using high-density surface electromyography anal and vaginal probes on large patient cohorts have been recently reported. Influences of vaginal delivery on the distribution of innervation zones of pelvic floor muscles are clarified, providing useful guidance for a better protection of women during delivery. We are now in a period of transition to advanced diagnostic and predictive pelvic floor medicine. Our findings highlight the application of diffusion tensor imaging, computational models with consideration of active pelvic floor muscle contraction, high-density surface electromyography, and their potential integration, as tools to push the boundary of our knowledge in pelvic floor support and better shape current clinical practice.

On the Methodological Implications of Extracting Muscle Synergies from Human Locomotion

We investigated the influence of three different high-pass (HP) and low-pass (LP) filtering conditions and a Gaussian (GNMF) and inverse-Gaussian (IGNMF) non-negative matrix factorization algorithm on the extraction of muscle synergies from myoelectric signals during human walking and running. To evaluate the effects of signal recording and processing on the outcomes, we analyzed the intraday and interday computation reliability. Results show that the IGNMF achieved a significantly higher reconstruction quality and on average needs one less synergy to sufficiently reconstruct the original signals compared to the GNMF. For both factorizations, the HP with a cut-off frequency of 250[Formula: see text]Hz significantly reduces the number of synergies. We identified the filter configuration of fourth order, HP 50[Formula: see text]Hz and LP 20[Formula: see text]Hz as the most suitable to minimize the combination of fundamental synergies, providing a higher reliability across all filtering conditions even if HP 250[Formula: see text]Hz is excluded. Defining a fundamental synergy as a single-peaked activation pattern, for walking and running we identified five and six fundamental synergies, respectively using both algorithms. The variability in combined synergies produced by different filtering conditions and factorization methods on the same data set suggests caution when attributing a neurophysiological nature to the combined synergies.

Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition

Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user’s intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.

Imaging three-dimensional innervation zone distribution in muscles from M-wave recordings

OBJECTIVE

To localize neuromuscular junctions in skeletal muscles in vivo which is of great importance in understanding, diagnosing and managing of neuromuscular disorders.

APPROACH

A three-dimensional global innervation zone imaging technique was developed to characterize the global distribution of innervation zones, as an indication of the location and features of neuromuscular junctions, using electrically evoked high-density surface electromyogram recordings.

MAIN RESULTS

The performance of the technique was evaluated in the biceps brachii of six intact human subjects. The geometric centers of the distributions of the reconstructed innervation zones were determined with a mean distance of 9.4  ±  1.4 cm from the reference plane, situated at the medial epicondyle of the humerus. A mean depth was calculated as 1.5  ±  0.3 cm from the geometric centers to the closed points over the skin. The results are consistent with those reported in previous histology studies. It was also found that the volumes and distributions of the reconstructed innervation zones changed as the stimulation intensities increased until the supramaximal muscle response was achieved.

SIGNIFICANCE

Results have demonstrated the high performance of the proposed imaging technique in noninvasively imaging global distributions of the innervation zones in the three-dimensional muscle space in vivo, and the feasibility of its clinical applications, such as guiding botulinum toxin injections in spasticity management, or in early diagnosis of neurodegenerative progression of amyotrophic lateral sclerosis.

Spatial characterization of innervation zones under electrically elicited M-wave

The three dimensional (3D) innervation zone (IZ) imaging approach (3DIZI) has been developed in our group to localize the IZ of a particular motor unit (MU) from its motor unit action potentials decomposed from high-density surface electromyography (EMG) recordings. In this study, the developed 3DIZI approach was combined with electrical stimulation to investigate global distributions of IZs in muscles from electrically elicited M-wave recordings. Electrical stimulations were applied to the musculocutaneous nerve to activate supramaximal muscle response of the biceps brachii in one healthy subject, and high-density (128 channels) surface EMG signals of the biceps brachii muscles were recorded. The 3DIZI approach was then employed to image the IZ distribution of IZs in the 3D space of the biceps brachii. The performance of the M-wave based 3DIZI approach was evaluated with different stimulation intensities. Results show that the reconstructed IZs under supramaximal stimulation are spatially distributed in the center region of muscle belly which is consistent with previous studies. With sub-maximal stimulation intensity, the imaged IZ centers became more proximally and deeply located. The proposed M-wave based 3DIZI approach demonstrated its capability of imaging global distribution of IZs in muscles, which provide valuable information for clinical applications such as guiding botulinum toxin injection in treating muscle spasticity.

CHARACTERIZATION OF MOTOR UNIT AT DIFFERENT STRENGTHS WITH MULTI-CHANNEL SURFACE ELECTROMYOGRAPHY

In this study, specific changes in electromyographic characteristics of individual motor units (MUs) associated with different muscle contraction forces are investigated using multi-channel surface electromyography (SEMG). The gradient convolution kernel compensation (GCKC) algorithm is employed to separate individual MUs from their surface interferential electromyography (EMG) signals and provide the discharge instants, which is later used in the spike-triggered averaging (STA) techniques to obtain the complete waveform. The method was tested on experimental SEMG signals acquired during constant force contractions of biceps brachii muscles in five subjects. Electromyographic characteristics including the recruitment number, waveform amplitude, discharge pattern and innervation zone (IZ) are studied. Results show that changes in the action potential of single MU with different contraction force levels are consistent with those for all MUs, and that the amplitude of MU action potentials (MUAPs) provides a useful estimate of the muscle contraction forces.

Motor unit number estimation based on high-density surface electromyography decomposition

OBJECTIVE

To advance the motor unit number estimation (MUNE) technique using high density surface electromyography (EMG) decomposition.

METHODS

The K-means clustering convolution kernel compensation algorithm was employed to detect the single motor unit potentials (SMUPs) from high-density surface EMG recordings of the biceps brachii muscles in eight healthy subjects. Contraction forces were controlled at 10%, 20% and 30% of the maximal voluntary contraction (MVC). Achieved MUNE results and the representativeness of the SMUP pools were evaluated using a high-density weighted-average method.

RESULTS

Mean numbers of motor units were estimated as 288±132, 155±87, 107±99 and 132±61 by using the developed new MUNE at 10%, 20%, 30% and 10-30% MVCs, respectively. Over 20 SMUPs were obtained at each contraction level, and the mean residual variances were lower than 10%.

CONCLUSIONS

The new MUNE method allows a convenient and non-invasive collection of a large size of SMUP pool with great representativeness. It provides a useful tool for estimating the motor unit number of proximal muscles.

SIGNIFICANCE

The present new MUNE method successfully avoids the use of intramuscular electrodes or multiple electrical stimuli which is required in currently available MUNE techniques; as such the new MUNE method can minimize patient discomfort for MUNE tests.

Functional mapping of the pelvic floor and sphincter muscles from high-density surface EMG recordings

INTRODUCTION AND HYPOTHESIS

Knowledge of the innervation of pelvic floor and sphincter muscles is of great importance to understanding the pathophysiology of female pelvic floor dysfunctions. This report presents our high-density intravaginal and intrarectal electromyography (EMG) probes and a comprehensive innervation zone (IZ) imaging technique based on high-density EMG readings to characterize the IZ distribution.

METHODS

Both intravaginal and intrarectal probes are covered with a high-density surface electromyography electrode grid (8 × 8). Surface EMG signals were acquired in ten healthy women performing maximum voluntary contractions of their pelvic floor. EMG decomposition was performed to separate motor-unit action potentials (MUAPs) and then localize their IZs.

RESULTS

High-density surface EMG signals were successfully acquired over the vaginal and rectal surfaces. The propagation patterns of muscle activity were clearly visualized for multiple muscle groups of the pelvic floor and anal sphincter. During each contraction, up to 218 and 456 repetitions of motor units were detected by the vaginal and rectal probes, respectively. MUAPs were separated with their IZs identified at various orientations and depths.

CONCLUSIONS

The proposed probes are capable of providing a comprehensive mapping of IZs of the pelvic floor and sphincter muscles. They can be employed as diagnostic and preventative tools in clinical practices.

Pre-contraction dynamic electrical impedance myography of the forearm finger flexors

Electrical activity in the sensory-motor and supplementary motor areas of the cerebral cortex is known to occur during a 'readiness interval', extending up to 2 s before the relevant muscle actually contracts. This paper presents evidence that there are also changes in the properties of the muscle itself during a similar preparatory period, as revealed by dynamic electrical impedance myography. 11 healthy subjects aged 23.5 ± 2.5 years were asked to perform a series of isometric gripping exercises during which the force, resistance and reactance of the forearm finger flexor muscles were monitored. A change in reactance, ΔX, or resistance, ΔR, which occurred before the generation of force, ΔF, was dubbed a 'PIC', shorthand for precontraction impedance change (subject to criteria to rule out the possibility of simple 'noise'), of which 1206 qualified in the entire subject cohort. Such PIC's are statistically well correlated when expressed in terms of differences between PIC and force onset times (r ≈ 0.9, p ≈ 0). This is demonstrated using a variation on the 'computer of average transients' method. Precontraction impedance changes (PICs) occurring as much as 2 s before the onset of force generation were found, in qualitative agreement with precontraction EEG activity reported in the literature. Also, a subset of PIC's was found in which the scaled and time-shifted ΔX(t) was virtually identical to ΔF(t). Since the occurrence and timing of all the PICs depend on oral commands, it is clear that the auditory cortex is likely involved, but the detailed mechanism coupling brain activity with PICs is not known.

Assessment of urethral support using MRI-derived computational modeling of the female pelvis

INTRODUCTION AND HYPOTHESIS

This study aimed to assess the role of individual anatomical structures and their combinations to urethral support function.

METHODS

A realistic pelvic model was developed from an asymptomatic female patient's magnetic resonance (MR) images for dynamic biomechanical analysis using the finite element method. Validation was performed by comparing simulation results with dynamic MR imaging observations. Weaknesses of anatomical support structures were simulated by reducing their material stiffness. Urethral mobility was quantified by examining urethral axis excursion from rest to the final state (intra-abdominal pressure = 100 cmH2O). Seven individual support structures and five of their combinations were studied.

RESULT

Among seven urethral support structures, we found that weakening the vaginal walls, puborectalis muscle, and pubococcygeus muscle generated the top three largest urethral excursion angles. A linear relationship was found between urethral axis excursions and intra-abdominal pressure. Weakening all three levator ani components together caused a larger weakening effect than the sum of each individually weakened component, indicating a nonlinearly additive pattern. The pelvic floor responded to different weakening conditions distinctly: weakening the vaginal wall developed urethral mobility through the collapsed vaginal canal, while weakening the levator ani showed a more uniform pelvic floor deformation.

CONCLUSIONS

The computational modeling and dynamic biomechanical analysis provides a powerful tool to better understand the dynamics of the female pelvis under pressure events. The vaginal walls, puborectalis, and pubococcygeus are the most important individual structures in providing urethral support. The levator ani muscle group provides urethral support in a well-coordinated way with a nonlinearly additive pattern.