Segmental infralesional pathological spontaneous activity in subacute traumatic spinal cord injury.

INTRODUCTION/AIMS: There is a dearth of knowledge regarding the status of infralesional lower motor neurons (LMNs) in individuals with traumatic cervical spinal cord injury (SCI), yet there is a growing need to understand how the spinal lesion impacts LMNs caudal to the lesion epicenter, especially in the context of nerve transfer surgery to restore several key upper limb functions. Our objective was to determine the frequency of pathological spontaneous activity (PSA) at, and below, the level of spinal injury, to gain an understanding of LMN health below the spinal lesion. METHODS: Ninety-one limbs in 57 individuals (53 males, mean age = 44.4 ± 16.9 years, mean duration from injury = 3.4 ± 1.4 months, 32 with motor complete injuries), were analyzed. Analysis was stratified by injury level as (1) C4 and above, (2) C5, and (3) C6-7. Needle electromyography was performed on representative muscles innervated by the C5-6, C6-7, C7-8, and C8-T1 nerve roots. PSA was dichotomized as present or absent. Data were pooled for the most caudal infralesional segment (C8-T1). RESULTS: A high frequency of PSA was seen in all infralesional segments. The pooled frequency of PSA for all injury levels at C8-T1 was 68.7% of the limbs tested. There was also evidence of PSA at the rostral border of the neurological level of injury, with 58.3% of C5-6 muscles in those with C5-level injuries. DISCUSSION: These data support a high prevalence of infralesional LMN abnormalities following SCI, which has implications to nerve transfer candidacy, timing of the intervention, and donor nerve options.

Clinical electrodiagnostic evaluation for nerve transfer surgery in spinal cord injury: a new indication and clinical pearls.

In this review, we highlight the important role of the clinical electrodiagnostic (EDX) evaluation after cervical spinal cord injury (SCI). Our discussion focuses on the need for timely, frequent, and accurate EDX evaluations in the context of nerve transfer surgery to restore critical upper limb functions, including elbow extension, hand opening, and hand closing. The EDX evaluation is crucial to define the extent of lower motor neuron lesions and determine candidacy for surgery. We also discuss the important role of the postoperative EDX evaluation in determining prognosis and supporting rehabilitation. We propose a practical framework for EDX evaluation in this clinical setting.

Resolving the effect of wrist position on myoelectric pattern recognition control.

BACKGROUND: The use of pattern recognition-based methods to control myoelectric upper-limb prostheses has been well studied in individuals with high-level amputations but few studies have demonstrated that it is suitable for partial-hand amputees, who often possess a functional wrist. This study's objective was to evaluate strategies that allow partial-hand amputees to control a prosthetic hand while allowing retain wrist function. METHODS: EMG data was recorded from the extrinsic and intrinsic hand muscles of six non-amputees and two partial-hand amputees while they performed 4 hand motions in 13 different wrist positions. The performance of 4 classification schemes using EMG data alone and EMG data combined with wrist positional information was evaluated. Using recorded wrist positional data, the relationship between EMG features and wrist position was modeled and used to develop a wrist position-independent classification scheme. RESULTS: A multi-layer perceptron artificial neural network classifier was better able to discriminate four hand motion classes in 13 wrist positions than a linear discriminant analysis classifier (p = 0.006), quadratic discriminant analysis classifier (p < 0.0001) and a linear perceptron artificial neural network classifier (p = 0.04). The addition of wrist position data to EMG data significantly improved performance (p < 0.001). Training the classifier with the combination of extrinsic and intrinsic muscle EMG data performed significantly better than using intrinsic (p < 0.0001) or extrinsic muscle EMG data alone (p < 0.0001), and training with intrinsic muscle EMG data performed significantly better than extrinsic muscle EMG data alone (p < 0.001). The same trends were observed for amputees, except training with intrinsic muscle EMG data, on average, performed worse than the extrinsic muscle EMG data. We propose a wrist position-independent controller that simulates data from multiple wrist positions and is able to significantly improve performance by 48-74% (p < 0.05) for non-amputees and by 45-66% for partial-hand amputees, compared to a classifier trained only with data from a neutral wrist position and tested with data from multiple positions. CONCLUSIONS: Sensor fusion (using EMG and wrist position information), non-linear artificial neural networks, combining EMG data across multiple muscle sources, and simulating data from different wrist positions are effective strategies for mitigating the wrist position effect and improving classification performance.

Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control.

Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

Optimizing pattern recognition-based control for partial-hand prosthesis application.

Partial-hand amputees often retain good residual wrist motion, which is essential for functional activities involving use of the hand. Thus, a crucial design criterion for a myoelectric, partial-hand prosthesis control scheme is that it allows the user to retain residual wrist motion. Pattern recognition (PR) of electromyographic (EMG) signals is a well-studied method of controlling myoelectric prostheses. However, wrist motion degrades a PR system's ability to correctly predict hand-grasp patterns. We studied the effects of (1) window length and number of hand-grasps, (2) static and dynamic wrist motion, and (3) EMG muscle source on the ability of a PR-based control scheme to classify functional hand-grasp patterns. Our results show that training PR classifiers with both extrinsic and intrinsic muscle EMG yields a lower error rate than training with either group by itself (p<0.001); and that training in only variable wrist positions, with only dynamic wrist movements, or with both variable wrist positions and movements results in lower error rates than training in only the neutral wrist position (p<0.001). Finally, our results show that both an increase in window length and a decrease in the number of grasps available to the classifier significantly decrease classification error (p<0.001). These results remained consistent whether the classifier selected or maintained a hand-grasp.