José Zariffa

Feasibility and efficacy of upper limb robotic rehabilitation in a subacute cervical spinal cord injury population

Study design: Multi-center pilot study.Objectives:To investigate the use of an upper limb robotic rehabilitation device (Armeo Spring, Hocoma AG, Switzerland) in a subacute cervical spinal cord injury (SCI) population.Setting: Two Canadian inpatient rehabilitation centers.Methods:Twelve subjects (motor level C4–C6, ASIA Impairment Scale A–D) completed the training, which consisted of 16.1±4.6 sessions over 5.2±1.4 weeks. Two types of outcomes were recorded: (1) feasibility of incorporating the device into an inpatient rehabilitation program (compliance with training schedule, reduction in therapist time required and subject questionnaires) and (2) efficacy of the robotic rehabilitation for improving functional outcomes (Graded and Redefined Assessment of Strength, Sensibility and Prehension (GRASSP), action research arm test, grip dynamometry and range of motion).Results:By the end of the training period, the robot-assisted training was shown to require active therapist involvement for 25±11% (mean±s.d.) of the total session time. In the group of all subjects and in a subgroup composed of motor-incomplete subjects, no statistically significant differences were found between intervention and control limbs for any of the outcome measures. In a subgroup of subjects with partial hand function at baseline, the GRASSP-Sensibility component showed a statistically significant increase (6.0±1.6 (mean±s.e.m.) point increase between baseline and discharge for the intervention limbs versus 1.9±0.9 points for the control limbs).Conclusion:The pilot results suggest that individuals with some preserved hand function after SCI may be better candidates for rehabilitation training using the Armeo Spring device.

Relationship Between Clinical Assessments of Function and Measurements From an Upper-Limb Robotic Rehabilitation Device in Cervical Spinal Cord Injury

Upper limb robotic rehabilitation devices can collect quantitative data about the users movements. Identifying relationships between robotic sensor data and manual clinical assessment scores would enable more precise tracking of the time course of recovery after injury and reduce the need for time-consuming manual assessments by skilled personnel. This study used measurements from robotic rehabilitation sessions to predict clinical scores in a traumatic cervical spinal cord injury (SCI) population. A retrospective analysis was conducted on data collected from subjects using the Armeo Spring (Hocoma, AG) in three rehabilitation centers. Fourteen predictive variables were explored, relating to range-of-motion, movement smoothness, and grip ability. Regression models using up to four predictors were developed to describe the following clinical scores: the GRASSP (consisting of four sub-scores), the ARAT, and the SCIM. The resulting adjusted R^2 value was highest for the GRASSP “Quantitative Prehension” component (0.78), and lowest for the GRASSP “Sensibility” component (0.54). In contrast to comparable studies in stroke survivors, movement smoothness was least beneficial for predicting clinical scores in SCI. Prediction of upper-limb clinical scores in SCI is feasible using measurements from a robotic rehabilitation device, without the need for dedicated assessment procedures.

Erratum to “Influence of the Number and Location of Recording Contacts on the Selectivity of a Nerve Cuff Electrode”

A 56-contact matrix nerve cuff electrode (seven rings with eight contacts each) was used to obtain recordings from the rat sciatic nerve, which were then discriminated as originating from one of three fascicles (tibial, peroneal, and sural branches). The influence of the number and location of the recording contacts on the classification accuracy was studied. The performance of a classifier was shown to be superior when data was available from all 56 contacts, compared to when only the eight contacts of the middle ring were used (as in previously proposed multicontact tripolar cuff designs). By examining the performance variations as contacts were included one at a time (in order of decreasing positive impact on performance), it was further shown that the matrix configuration could outperform the single-ring configuration with only a small number of contacts. We can therefore conclude that the performance improvement is not due to the sheer number of contacts, but rather to the possibility of selecting the most informative locations around the nerve. The results could have important implications for the design and use of multicontact nerve cuff electrodes.

Application of singular spectrum-based change-point analysis to EMG-onset detection

While many approaches have been proposed to identify the signal onset in EMG recordings, there is no standardized method for performing this task. Here, we propose to use a change-point detection procedure based on singular spectrum analysis to determine the onset of EMG signals. This method is suitable for automated real-time implementation, can be applied directly to the raw signal, and does not require any prior knowledge of the EMG signals properties. The algorithm proposed by Moskvina and Zhigljavsky (2003) was applied to EMG segments recorded from wrist and trunk muscles. Wrist EMG data was collected from 9 Parkinsons disease patients with and without tremor, while trunk EMG data was collected from 13 healthy able-bodied individuals. Along with the change-point detection analysis, two threshold-based onset detection methods were applied, as well as visual estimates of the EMG onset by trained practitioners. In the case of wrist EMG data without tremor, the change-point analysis showed comparable or superior frequency and quality of detection results, as compared to other automatic detection methods. In the case of wrist EMG data with tremor and trunk EMG data, performance suffered because other changes occurring in these signals caused larger changes in the detection statistic than the changes caused by the initial muscle activation, suggesting that additional criteria are needed to identify the onset from the detection statistic other than its magnitude alone. Once this issue is resolved, change-point detection should provide an effective EMG-onset detection method suitable for automated real-time implementation.

Test-retest reliability of contact heat-evoked potentials from cervical dermatomes.

Summary The purpose of this study was to investigate the test–retest reliability of contact heat-evoked potentials (CHEPs) in neurologically healthy subjects from cervical dermatomes (C4–C8). Seventeen individuals underwent test–retest examination of cervical CHEPs. Peak latencies and peak-to-peak amplitude of N2–P2 and ratings of perceived intensity were analyzed using test–retest reliability statistics (intraclass correlation coefficients [ICCs] and Bland–Altman confidence parameters). For comparison, a group of similar age and gender was also examined with dermatomal somatosensory-evoked potentials (dSSEPs, n = 17). The ICC values for CHEP latency and amplitude parameters were significant (P < 0.05) and corresponded to at least “fair” reliability, while peak-to-peak amplitude demonstrated “substantial” (≥0.81) reliability for all dermatomes. The coefficients of repeatability (i.e., 2SD of the difference between examinations) confirm that CHEPs and dSSEPs are reliable according to measures of latency. Superior peak-to-peak amplitude test–retest reliability was found for CHEPs. In conclusion, the test–retest reliability of dSSEP and CHEP parameters supports the fact that these outcomes can be used to objectively track changes in spinal conduction in the dorsal column and spinothalamic tract, respectively. The reliable acquisition of CHEPs may depend on the intensity of the sensation reported by the subject for a given area of skin stimulated.

Use of an Experimentally Derived Leadfield in the Peripheral Nerve Pathway Discrimination Problem

The task of discriminating the neural pathways responsible for the activity recorded using a multi-contact nerve cuff electrode has recently been approached as an inverse problem of source localization, similar to EEG source localization. A major drawback of this method is that it requires a model of the nerve, and that the localization performance is highly dependent on the accuracy of this model. Using recordings from a 56-contact “matrix” cuff electrode placed on a rat sciatic nerve, we investigated a method that eliminates the need for a model, and uses instead an “experimental” leadfield constructed from a training set of experimental recordings. The resulting pathway-identification task is solved using an inverse problem framework. The experimental leadfield approach was able to identify the correct branch in cases in which a single fascicle was active with a success rate of 94.2%, but was not able to reliably identify combinations of fascicles. Nevertheless, the proposed methodology provides a framework for the study of multi-pathway discrimination, within which methods to improve performance can be investigated. Specifically, the influence of nerve anatomy and electrode design should be examined, and regularization approaches better suited to this novel inverse problem should be sought.

Localization of Active Pathways in Peripheral Nerves: A Simulation Study

A methodology is investigated for determining the location of active pathways in a peripheral nerve using measurements from a multicontact cuff electrode. The problem is treated as an inverse problem of source localization and solved using the sLORETA algorithm, developed for the electroencephalogram/magnetoencephalogram source localization problem. Simulated measurements are generated corresponding to action potentials traveling along either one or three pathways in a rat sciatic nerve. The performance of the proposed methodology using these measurements is evaluated in terms of localization error, missed pathways, and spurious pathways. The source localization performance when assuming an idealized nerve anatomy is compared to that when the correct anatomy is known. The effect of a spatio-temporal constraint based on the nerve anatomy and electrophysiology is also investigated. The approach in its present form was not found to be sufficiently reliable for subfascicular localization in practice, due to mean localization errors in the 140-180 mum range, high numbers of spurious pathways, and low resolution. Nonetheless, the constraints were shown to produce a marked reduction in the number of spurious pathways. Conditions under which the source localization approach may be useful for peripheral nerves are discussed.

Short‐Term Neuroplastic Effects of Brain‐Controlled and Muscle‐Controlled Electrical Stimulation

Functional electrical stimulation (FES) has been shown to facilitate the recovery of grasping function in individuals with incomplete spinal cord injury. Neurophysiological theory suggests that this benefit may be further enhanced by a more consistent pairing of the voluntary commands sent from the users brain down their spinal cord with the electrical stimuli applied to the users periphery. The objective of the study was to compare brain‐machine interfaces (BMIs)‐controlled and electromyogram (EMG)‐controlled FES therapy to three more well‐researched therapies, namely, push button‐controlled FES therapy, voluntary grasping (VOL), and BMI‐guided voluntary grasping.

Hand contour detection in wearable camera video using an adaptive histogram region of interest.

BackgroundMonitoring hand function at home is needed to better evaluate the effectiveness of rehabilitation interventions. Our objective is to develop wearable computer vision systems for hand function monitoring. The specific aim of this study is to develop an algorithm that can identify hand contours in video from a wearable camera that records the user’s point of view, without the need for markers.MethodsThe two-step image processing approach for each frame consists of: (1) Detecting a hand in the image, and choosing one seed point that lies within the hand. This step is based on a priori models of skin colour. (2) Identifying the contour of the region containing the seed point. This is accomplished by adaptively determining, for each frame, the region within a colour histogram that corresponds to hand colours, and backprojecting the image using the reduced histogram.ResultsIn four test videos relevant to activities of daily living, the hand detector classification accuracy was 88.3%. The contour detection results were compared to manually traced contours in 97 test frames, and the median F-score was 0.86.ConclusionThis algorithm will form the basis for a wearable computer-vision system that can monitor and log the interactions of the hand with its environment.

Application of EEG source localization algorithms to the monitoring of active pathways in peripheral nerves

Improved techniques for identifying active pathways in peripheral nerves using extraneural measurements would have numerous applications in the fields of neuroprostheses, neural system engineering, and diagnostics. In this study, we propose to approach this issue as an inverse problem of bioelectric source localization, using measurements from a multi-contact nerve cuff electrode. This problem is a modified version of the electroencephalogram / magneto-encephalogram (EEG/MEG) source localization problem. We therefore evaluate the performance of two well-known EEG/MEG source localization algorithms, namely sLORETA and sFOCUSS, when they are applied to the peripheral nerve problem. sLORETA is found to be a potentially viable approach, albeit with limited resolution, while sFOCUSS is found to produce too many spurious pathways in the presence of noise.