Rakesh Pilkar

EMG of the tibialis anterior demonstrates a training effect after utilization of a foot drop stimulator

BACKGROUND Functional Electrical Stimulation (FES) applied through a foot drop stimulator (FDS) is a rehabilitation intervention that can stimulate the common peroneal nerve to provide dorsiflexion at the correct timing during gait. OBJECTIVE To determine if FES applied to the peroneal nerve during walking through a FDS would effectively retrain the electromyographic temporal activation of the tibialis anterior in individuals with stroke. METHODS Surface electromyography (EMG) were collected bilaterally from the tibialis anterior (TA) while participants (n = 4) walked with and without the FDS at baseline and 4 weeks. Comparisons were made between stimulation timing and EMG activation timing to produce a burst duration similarity index (BDSI). RESULTS At baseline, participants displayed variable temporal activation of the TA. At 4 weeks, TA activation during walking without the FDS more closely resembled the pre-programmed FDS timing demonstrated by an increase in BDSI scores in all participants (P = 0.05). CONCLUSIONS Continuous use of FDS during a task specific movement can re-train the neuromuscular system. After 4 weeks of utilization the FDS trained the TA to replicate the programmed temporal activation patterns. These findings begin to establish the FDS as a rehabilitation intervention that may facilitate recovery rather than just compensate for stroke related gait impairments due to foot drop.

Empirical mode decomposition as a tool to remove the function Electrical stimulation artifact from surface electromyograms: Preliminary investigation

Rectification of surface EMGs during electrical stimulations (ES) is still a problem to be solved. The broad band frequency components of ES artifact overlap with the EMG spectrum, make this task challenging. In this study, we investigate the potential use of empirical mode decomposition (EMD) method to remove the stimulus artifact from surface EMGs collected during such applications. We hypothesize that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic modes which can be used to isolate ES artifact. Basic EMD is tested on two signals - ES induced EMG and EMG of voluntary contractions added with simulated ES signal. The algorithm isolates the EMG from ES artifact with considerable success. Further, the EMD method along with the energy operator -TKEO gives even better representation of the EMG signal. However, some high frequency data was lost during reconstruction process. Hence, there is further need to investigate the relationship between the EMD parameters and stimulus artifact properties so that the algorithm can be optimized to reconstruct pure artifact free EMG signal with minimum lost of data.

Application of Empirical Mode Decomposition Combined With Notch Filtering for Interpretation of Surface Electromyograms During Functional Electrical Stimulation

The goal of this paper is to demonstrate a novel approach that combines Empirical Mode Decomposition (EMD) with Notch filtering to remove the electrical stimulation (ES) artifact from surface electromyogram (EMG) data for interpretation of muscle responses during functional electrical stimulation (FES) experiments. FES was applied to the rectus femoris (RF) muscle unilaterally of six able bodied (AB) and one individual with spinal cord injury (SCI). Each trial consisted of three repetitions of ES. We hypothesized that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic mode functions (IMFs) which can be further used to isolate electrical stimulation (ES) artifact. A basic EMD algorithm was used to decompose the EMG signals collected during FES into IMFs for each repetition separately. IMFs most contaminated by ES were identified based on the standard deviation (SD) of each IMF. Each artifact IMF was Notch filtered to filter ES harmonics and added to remaining IMFs containing pure EMG data to get a version of a filtered EMG signal. Of all such versions of filtered signals generated from each artifact IMF, the one with maximum signal to noise ratio (SNR) was chosen as the final output. The validity of the filtered signal was assessed by quantitative metrics, 1) root mean squared error (RMSE) and signal to noise (SNR) ratio values obtained by comparing a clean EMG and EMD-Notch filtered signal from the combination of simulated ES and clean EMG and, 2) using EMG-force correlation analysis on the data collected from AB individuals. Finally, the potential applicability of this algorithm on a neurologically impaired population was shown by applying the algorithm on EMG data collected from an individual with SCI. EMD combined with Notch filtering successfully extracted the EMG signal buried under ES artifact. Filtering performance was validated by smaller RMSE values and greater SNR post filtering. The amplitude values of the filtered EMG signal were seen to be consistent for three repetitions of ES and there was no significant difference among the repetition for all subjects. For the individual with a SCI the algorithm was shown to successfully isolate the underlying bursts of muscle activations during FES. The data driven nature of EMD algorithm and its ability to act as a filter bank at different bandwidths make this method extremely suitable for dissecting ES induced EMG into IMFs. Such IMFs clearly show the presence of ES artifact at different intensities as well as pure artifact free EMG. This allows the application of Notch filters to IMFs containing ES artifact to further isolate the EMG. As a result of such stepwise approach, the extraction of EMG is achieved with minimal data loss. This study provides a unique approach to dissect and interpret the EMG signal during FES applications.

Validation of empirical mode decomposition combined with notch filtering to extract electrical stimulation artifact from surface electromyograms during functional electrical stimulation

This paper presents the validity of Empirical Mode Decomposition (EMD) combined with Notch filtering to remove the electrical stimulation (ES) artifact from surface electromyogram (EMG) data for interpretation of muscle responses during Functional Electrical Stimulation (FES) experiments. We hypothesized that the EMD algorithm provides a suitable platform for decomposing the EMG signal into physically meaningful intrinsic mode functions (IMFs) which can be further used to isolate electrical stimulation (ES) artifact. The basic EMD algorithm was used to decompose the ES induced EMG signals into IMFs. IMFs most contaminated by ES were identified based on the standard deviation (SD) criterion. An IMF with the maximum signal to noise ratio (SNR) was Notch filtered and added to IMFs containing pure EMG data to get the filtered EMG signal. The method was tested on 5 able bodied (AB) and 2 spinal cord injured (SCI) participants. The validity of the filtered signal was assessed by normalized root mean squared error (NRMSE) and signal to noise (SNR) ratio values obtained by comparing a clean EMG collected during maximum volitional contraction (MVC) and EMD-Notch filtered signal from the combination of a clean EMG with i) simulated ES and, ii) real ES with no activation generated at different ES amplitudes. The results showed that the EMD-Notch filtering approach was successful, reliable and repeatable in extracting pure muscle responses during ES showing improved values for NRMSE and SNR in both AB and SCI individuals.

Postural responses after utilization of a computerized biofeedback based intervention aimed at improving static and dynamic balance in traumatic brain injury: A case study

Balance dysfunction is one of the most disabling aspects of Traumatic Brain Injury (TBI). Without rapid transmission and accurate perception of somatosensory inputs, the automatic postural responses required during standing may be delayed or absent after TBI which can lead to instability. Further, the sensitivity level to which environmental perturbations can be detected is also vital, as the central nervous system will only employ balance control strategies when it perceives a change in equilibrium. Such undetectable perturbations, however small they may be, can result in fatal falls, especially after TBI. In this investigation we used a novel computerized biofeedback based (CBB) intervention aimed at improving perception of external perturbations, and static and dynamic balance in a single male participant with severe TBI. We used an adaptive single interval adjustment matrix (SIAM) protocol to determine the perception of perturbation threshold (PPT) at baseline (1 day pre-intervention) and follow up (1 day post-intervention). External perturbations were provided through sinusoidal translations of 0.5 Hz to the base of support in anterior-posterior direction. Outcome measures included PPT, the Berg balance scale (BBS) and bilateral surface electromyography (EMG) of the lower limbs at baseline and follow up. PPT assessment post intervention showed a decrease in PPT, suggesting an improvement in the ability (gain of 0.42 mm) to detect (even smaller) perturbations which were not perceivable prior to the intervention. There was a significant increase in BBS (6 points) at follow up. The participant demonstrated increased muscle activation for the right gastrocnemius, left soleus, right bicep femoris and left vastus lateralis muscles at follow up. This investigation demonstrate the potential use of the CBB intervention for improving interpretation and organization of multisensory information in a task specific environment to improve balance dysfunction post TBI.Balance dysfunction is one of the most disabling aspects of Traumatic Brain Injury (TBI). Without rapid transmission and accurate perception of somatosensory inputs, the automatic postural responses required during standing may be delayed or absent after TBI which can lead to instability. Further, the sensitivity level to which environmental perturbations can be detected is also vital, as the central nervous system will only employ balance control strategies when it perceives a change in equilibrium. Such undetectable perturbations, however small they may be, can result in fatal falls, especially after TBI. In this investigation we used a novel computerized biofeedback based (CBB) intervention aimed at improving perception of external perturbations, and static and dynamic balance in a single male participant with severe TBI. We used an adaptive single interval adjustment matrix (SIAM) protocol to determine the perception of perturbation threshold (PPT) at baseline (1 day pre-intervention) and follow up (1 day post-intervention). External perturbations were provided through sinusoidal translations of 0.5 Hz to the base of support in anterior-posterior direction. Outcome measures included PPT, the Berg balance scale (BBS) and bilateral surface electromyography (EMG) of the lower limbs at baseline and follow up. PPT assessment post intervention showed a decrease in PPT, suggesting an improvement in the ability (gain of 0.42 mm) to detect (even smaller) perturbations which were not perceivable prior to the intervention. There was a significant increase in BBS (6 points) at follow up. The participant demonstrated increased muscle activation for the right gastrocnemius, left soleus, right bicep femoris and left vastus lateralis muscles at follow up. This investigation demonstrate the potential use of the CBB intervention for improving interpretation and organization of multisensory information in a task specific environment to improve balance dysfunction post TBI.

Cyclogram Based Symmetry Assessment of the Lower Limbs for Individuals With Post Stroke Hemiplegia

Design: Randomized controlled trials that compared the effect of BoNTA combined with UL rehabilitation and UL rehabilitation plus placebo or only rehabilitation were selected following a search of six databases and reference lists of related articles in December 2012. Two reviewers independently assessed study quality. Weighted mean differences (WMD) were used to assess the changes in spasticity, functional level, and pain in the UL. Setting: N/A. Participants: N/A. Interventions: N/A. Main Outcome Measure(s): N/A. Results: Five randomized controlled trialswith a total of 446patientswithUL spasticity were included in the systematic review. The assessments were performed 4 weeks and 12 weeks after BoNTA injection. Compared to UL rehabilitation only, BoNTA and UL rehabilitation resulted in decreased UL spasticity at 4weeks after injection using theModifiedAshworthScale (WMD -0.59, 95% CI -0.81 to 0.37). At 12 weeks, the difference in spasticity was not significant (WMD 0.26, 95% CI 0.09 to 0.43). There was no difference for measures of UL function or pain at 4 weeks and 12 weeks (all p>0.05). Conclusions: The combination of BoNTA with UL rehabilitation decreased spasticity to a greater extent than UL rehabilitation plus placebo or UL rehabilitation alone at the 4 week time point only. BoNTA administration did not impact UL function or pain at either the 4 or 12 week post-intervention time point.