Alterations in Spectral Attributes of Surface Electromyograms after Utilization of a Foot Drop Stimulator during Post-Stroke Gait

No clue what this says or how it can be used to get you recovered from foot drop.
http://journal.frontiersin.org/article/10.3389/fneur.2017.00449/full?

Rakesh Pilkar^1,2*, Arvind Ramanujam¹ and Karen J. Nolan^1,2

• ¹Human Performance and Engineering Research, Kessler Foundation, West Orange, NJ, United States
• ²New Jersey Medical School, Newark, NJ, United States

Background: A foot drop stimulator (FDS) is a rehabilitation intervention that stimulates the common peroneal nerve to facilitate ankle dorsiflexion at the appropriate time during post-stroke hemiplegic gait. Time–frequency analysis (TFA) of non-stationary surface electromyograms (EMG) and spectral variables such as instantaneous mean frequency (IMNF) can provide valuable information on the long-term effects of FDS intervention in terms of changes in the motor unit (MU) recruitment during gait, secondary to improved dorsiflexion.

Objective: The aim of this study was to apply a wavelet-based TFA approach to assess the changes in neuromuscular activation of the tibialis anterior (TA), soleus (SOL), and gastrocnemius (GA) muscles after utilization of an FDS during gait post-stroke.

Methods: Surface EMG were collected bilaterally from the TA, SOL, and GA muscles from six participants (142.9 ± 103.3 months post-stroke) while walking without the FDS at baseline and 6 months post-FDS utilization. Continuous wavelet transform was performed to get the averaged time–frequency distribution of band pass filtered (20–300 Hz) EMGs during multiple walking trials. IMNFs were computed during normalized gait and were averaged during the stance and swing phases. Percent changes in the energies associated with each frequency band of 25 Hz between 25 and 300 Hz were computed and compared between visits.

Results: Averaged time–frequency representations of the affected TA, SOL, and GA EMG show altered spectral attributes post-FDS utilization during normalized gait. The mean IMNF values for the affected TA were significantly lower than the unaffected TA at baseline (p = 0.026) and follow-up (p = 0.038) during normalized stance. The mean IMNF values significantly increased (p = 0.017) for the affected GA at follow-up during normalized swing. The frequency band of 250–275 Hz significantly increased in the energies post-FDS utilization for all muscles.

Conclusion: The application of wavelet-based TFA of EMG and outcome measures (IMNF, energy) extracted from the time–frequency distributions suggest alterations in MU recruitment strategies after the use of FDS in individuals with chronic stroke. This further establishes the efficacy of FDS as a rehabilitation intervention that may promote motor recovery in addition to treating the secondary complications of foot drop due to post-stroke hemiplegia.

Introduction

Stroke is one of the leading causes of serious and long-term disability, and foot drop is one of the most common disabling impairments resulting from hemiplegia due to stroke (1). Foot drop characterized by weakness and/or lack of voluntary control in the ankle and toe dorsiflexor muscles (2) can result in gait related deficiencies (decreased speed, a disruption in weight acceptance and transfer, asymmetry and instability), further limiting the activities of daily living (3, 4). The application of an ankle foot orthosis (AFO) to compensate for foot drop throughout the gait has been the common modality of treatment. Although AFOs have been shown to increase gait speed and functional ambulation (3, 5), as a rehabilitation intervention it is not targeted to restore muscle function (2).

Functional electrical stimulation (FES) has been evident as a targeted rehabilitation intervention that may promote motor recovery, especially when applied in a task-specific environment (6–9). FES applied to the common peroneal nerve through a foot drop stimulator (FDS) provides a focused excitation to the peroneal nerve to promote active ankle dorsiflexion during initial double support at heel strike, at pre-swing lift-off and during the swing phase of gait to sufficiently clear the foot (10–12). Using FDS to drive muscle groups in specific activation patterns during walking has been shown to improve strength, walking speed, spatiotemporal parameters, and retrain ankle dorsiflexor muscle [tibialis anterior (TA)] activation timings (2, 9, 13–16). These demonstrate the efficacy for FDS utilization in post-stroke rehabilitation, but they fail to precisely indicate how FDS technology can restore motor function (2, 12, 14, 16–20).

To understand the role of FDS-based gait rehabilitation in recovering motor function, it is important to understand the intrinsic electrophysiological modifications that may elicit the improvements in the muscle function. Surface electromyography is one of the most effective non-invasive tools, which provide easy access to the underlying physiological processes that cause the muscle to generate force, produce movement, and achieve any functional task (21). Electromyograms (EMG) data collected during gait can provide us with a quantitative measure of muscle activations and activation timings, which could be used to assess the level of improvement post-rehabilitation (10, 15, 22, 23). As a result, the application of various signal processing techniques to extract meaningful information from the EMG data has been an on-going process in stroke rehabilitation. To better understand the electrophysiological processes behind neuromuscular activations, it is essential to study these signals in time as well as frequency domain. Signal processing methods such as empirical mode decomposition (EMD) (24, 25) and wavelet analysis (26–28) have provided researchers tools to interpret non-stationary EMG data in time and frequency domain simultaneously. EMD has gained popularity for analyzing non-stationary signals and has been utilized in filtering EMG signals (25, 29, 30), time–frequency analysis (TFA) (25), fatigue analysis (31) due to its ability to decompose EMG signals into physically meaningful intrinsic mode functions (IMFs). Although EMD-based approach has advantages in analyzing EMG, time–frequency representations obtained using EMD-Hilbert transform could be excessively detailed, making it difficult to interpret, particularly for EMGs collected during dynamic movements such as gait. Although smoothing techniques have been suggested to obtain more continuous Hilbert spectrums, it has been suggested that such techniques may result in degradation of time–frequency resolution as well as physically meaningful content (24). In contrast, wavelet-based TFA provides more continues representation of the data and has been widely utilized to identify motor unit (MU) recruitment patterns (26), motor strategy patterns (28) and perform clinical assessments (27) during gait. In the current literature, these analyses have only been performed in individuals with cerebral palsy, diabetic neuropathy, ankle osteoarthritis, and healthy populations (26–28). The current investigation presents a novel application of wavelet-based TFA of EMG signals in individuals post-stroke during gait. In addition, wavelet-based TFA from lower extremity muscles is further analyzed to assess the neuromuscular changes occurring due to FDS-based gait retraining has not been done yet.

The purpose of this study was to apply a wavelet-based TFA approach to assess the neuromuscular changes in the TA, soleus (SOL), and gastrocnemius (GA) muscles after utilization of an FDS as a gait rehabilitation tool in individuals post-stroke. Changes in the spectral variable—instantaneous mean frequency (IMNF) and energies associated with bands of frequencies (25–300 Hz) extracted from time–frequency distributions of EMGs from TA, SOL, and GA muscles are compared to assess the alterations in MU recruitments post-FDS utilization. We hypothesize that the time–frequency distribution of targeted TA and indirectly stimulated SOL and GA muscles will show changes in time–frequency distribution (TFD) with increased mean IMNF and energy after utilization of an FDS.

Much more at link including wonderful equations for your doctor to explain.
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The continuous wavelet transform (CWT) has the following general definition:

W(a,b;x,ψ)=|a|−12∫−∞∞x(t)ψ∗(t−ba)dt

(1)

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Mean frequency (MNF) is an average frequency of a power density spectrum of a signal. IMNF can be computed from a TFD, W(f, t) as

IMNF(t)=∑Mj=1fjW(fj,t)∑Mj=1W(fj,t)

(2)

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The energy values were computed from the TFD, W obtained using CWT for a total of eleven frequency bands, each with a bandwidth of 25 Hz. The first band covered the frequencies from 25 to 50 Hz, the second band ranged from 51 to 75 Hz, and subsequent bands covering up to 300 Hz. Frequencies below 25 Hz and above 300 Hz were not considered for this analysis as the energies associated with these frequencies were negligible due to band pass filtering between 20 and 300 Hz. The TFD energy for each band was computed as a percentage of the total distribution energy as:

E(%)=100×∑fbf=faW(f,t)∑W(f,t)

(3)