Automatic signal and image-based assessments of spinal cord injury and treatments.

Spinal cord injury (SCI) is one of the most common sources of motor disabilities in humans that often deeply impact the quality of life in individuals with severe and chronic SCI. In this dissertation, we have developed advanced engineering tools to address three distinct problems that researchers, clinicians and patients are facing in SCI research. Particularly, we have proposed a fully automated stochastic framework to quantify the effects of SCI on muscle size and adipose tissue distribution in skeletal muscles by volumetric segmentation of 3-D MRI scans in individuals with chronic SCI as well as non-disabled individuals. We also developed a novel framework for robust and automatic activation detection, feature extraction and visualization of the spinal cord epidural stimulation (scES) effects across a high number of scES parameters to build individualized-maps of muscle recruitment patterns of scES. Finally, in the last part of this dissertation, we introduced an EMG time-frequency analysis framework that implements EMG spectral analysis and machine learning tools to characterize EMG patterns resulting in independent or assisted standing enabled by scES, and identify the stimulation parameters that promote muscle activation patterns more effective for standing. The neurotechnological advancements proposed in this dissertation have greatly benefited SCI research by accelerating the efforts to quantify the effects of SCI on muscle size and functionality, expanding the knowledge regarding the neurophysiological mechanisms involved in re-enabling motor function with epidural stimulation and the selection of stimulation parameters and helping the patients with complete paralysis to achieve faster motor recovery

Frequency Analysis of EMG Signals of Individuals with Spinal Cord Injury: Comparison Between FFT, STFT and Wavelet Methods

Introduction: Surface or wired electromyogram (EMG) signals from skeletal muscles of individuals with spinal cord injury (SCI) have been used to characterize muscle activation during the performance of various motor tasks. Transforming the EMG signal to frequency domain provides information about the frequency content of the signal. Fast Fourier transform (FFT) has been traditionally used to analyze EMG signals in frequency domain. However, FFT has issues such as assuming stationarity for the EMG signal and not being localized in time, so that it is not suited for representing efficiently abrupt changes in the signal. The short-time Fourier transform (STFT) was designed to increase the time resolution of FFT by selecting a fixed-size moving window and take the FFT for each segment; however, STFT may have poor resolution in time and/or frequency domains, depending on the window size. Continuous Wavelet transform (CWT) has been designed to overcome these limitations by being well-localized in time and frequency, representing accurately abrupt signal fluctuations. In this work, we are comparing the results of these three methods for analyzing the EMG signals and present the limitations of each method. Materials and Methods: SCI participants that have been implanted with a spinal cord epidural stimulation (ES) unit in order to modulate the excitability of lumbosacral spinal circuits, performed standing with and without stimulation. EMG signals were recorded from 16 leg muscles (8 muscles on each side) during standing. EMG signals related to stable standing conditions were divided into 10-second time intervals in order to obtain overall stationary signals, and FFT, STFT and CWT (Morlet wavelet) analyses were performed. The power spectral density (PSD), spectrogram and scalogram of the signals were calculated by taking the squared magnitude of FFT, STFT and CWT, respectively. By assuming the wide-sense stationarity, we calculated the mean spectral power values over the selected intervals for each frequency component for STFT and CWT. For a better visual comparison, the spectral power values were normalized to the highest values between these three methods. Results and Discussion: Power spectral values as a function of frequency calculated for EMG signals related to active and inactive muscles with and without ES are shown in Fig. 1. In the cases of EMG signals with muscle activity without ES (Fig.1 top-left), the mean frequency (MNF) values for FFT, STFT and CWT are 130.9 Hz, 131.2 Hz and 133.0 Hz respectively. This shows that CWT is more sensitive in describing high frequency contents of the muscle activation. In the case of muscles contracting with the help of ES, FFT and STFT predominantly present the spikes related to the ES frequency (20 Hz) and its harmonics, and disregard the frequency contents of motor evoked potentials. On the other hand, CWT is able to capture the continuous changes in frequency content of the ES induced evoked potentials that are the building blocks of the recorded EMG and the cause of muscle contraction. When the muscles are not active (with or without ES), noise is recorded from EMG electrodes. In this case, the CWT method shows that the signal power at higher frequencies (>350 Hz) is much greater than at lower frequencies. This finding is substantially different from the results of the two other methods, and suggests that wavelet analysis has the highest sensitivity to abrupt changes (i.e. noise) and it can discriminate between a signal that carries actual muscle response and noise. Conclusion: The differences that are presented in the results (and our calculations) indicate that the features that are commonly calculated in the frequency domain such as mean, median and dominant frequencies and total power are considerably different values for CWT compared to the other two methods. This suggests that specially for ES-evoked muscle responses, even if the signal could be considered stationary, wavelet provides a more accurate representation of the signal frequency components than FFT and STFT

Predictive modeling to assess the effectiveness of epidural stimulation parameters that promote standing in individuals with severe spinal cord injury

Introduction: Data mining techniques have growing applications in large datasets in healthcare to enable researchers and healthcare professionals to systematically use machine learning tools to identify patterns in the data and use it to improve protocols and predict future outcomes. We have been collecting EMG activity data during standing with spinal cord epidural stimulation for a decade on individuals with motor complete spinal cord injury (SCI). It is also a well-known fact that finding proper stimulation parameters (i.e. electrode combination, polarity, intensity and frequency) is a key factor in promoting independent standing with epidural stimulation. In this study, we have developed a predictive modeling framework to address the challenging task of stimulation parameters selection that leads to independent standing. Materials and Methods: Eleven individuals with chronic, clinically motor complete or sensory and motor complete SCI individuals are included in this study. The EMG signals were recorded from 16 proximal and distal leg muscles during the performance of standing task with epidural stimulation. We proposed a predictive modeling framework (Fig. 1) that uses spectral features of EMG and multiple classifiers to find neurophysiological patterns in the EMG recordings from 16 leg muscles that lead to independent standing. This framework then uses the trained models to predict the effectiveness of each set of stimulation parameters for promoting independent standing performance in 48 assisted standing events performed for 6 participants while different scES parameters were tested. Results and Discussion: We have shown that the trained KNN classifiers can perform with average accuracy of 96% to discriminate assisted standing from independent standing conditions. The proposed prediction algorithm can also score the performance of each investigated muscle based on the trained patterns when different stimulation parameters are selected (Fig. 2). The results of the prediction algorithm suggests that the proposed framework can provide reliable feedback regarding which stimulation parameters can improve the performance of each muscle that would subsequently lead to independent standing. Conclusion: The proposed framework in this study can fast-track the search for proper sets of stimulation parameters, and therefore improve standing motor recovery in the SCI population

A novel approach for automatic visualization and activation detection of evoked potentials induced by epidural spinal cord stimulation in individuals with spinal cord injury

<div><p>Voluntary movements and the standing of spinal cord injured patients have been facilitated using lumbosacral spinal cord epidural stimulation (scES). Identifying the appropriate stimulation parameters (intensity, frequency and anode/cathode assignment) is an arduous task and requires extensive mapping of the spinal cord using evoked potentials. Effective visualization and detection of muscle evoked potentials induced by scES from the recorded electromyography (EMG) signals is critical to identify the optimal configurations and the effects of specific scES parameters on muscle activation. The purpose of this work was to develop a novel approach to automatically detect the occurrence of evoked potentials, quantify the attributes of the signal and visualize the effects across a high number of scES parameters. This new method is designed to automate the current process for performing this task, which has been accomplished manually by data analysts through observation of raw EMG signals, a process that is laborious and time-consuming as well as prone to human errors. The proposed method provides a fast and accurate five-step algorithms framework for activation detection and visualization of the results including: conversion of the EMG signal into its 2-D representation by overlaying the located signal building blocks; de-noising the 2-D image by applying the Generalized Gaussian Markov Random Field technique; detection of the occurrence of evoked potentials using a statistically optimal decision method through the comparison of the probability density functions of each segment to the background noise utilizing log-likelihood ratio; feature extraction of detected motor units such as peak-to-peak amplitude, latency, integrated EMG and Min-max time intervals; and finally visualization of the outputs as Colormap images. In comparing the automatic method vs. manual detection on 700 EMG signals from five individuals, the new approach decreased the processing time from several hours to less than 15 seconds for each set of data, and demonstrated an average accuracy of 98.28% based on the combined false positive and false negative error rates. The sensitivity of this method to the signal-to-noise ratio (SNR) was tested using simulated EMG signals and compared to two existing methods, where the novel technique showed much lower sensitivity to the SNR.</p></div

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications. In this study, we developed a novel automatic 3-D approach for volumetric segmentation and quantitative assessment of thigh Magnetic Resonance Imaging (MRI) volumes in individuals with chronic SCI as well as non-disabled individuals. In this framework, subcutaneous adipose tissue, inter-muscular adipose tissue and total muscle tissue are segmented using linear combination of discrete Gaussians algorithm. Also, three thigh muscle groups were segmented utilizing the proposed 3-D Joint Markov Gibbs Random Field model that integrates first order appearance model, spatial information, and shape model to localize the muscle groups. The accuracy of the automatic segmentation method was tested both on SCI (N = 16) and on non-disabled (N = 14) individuals, showing an overall 0.93±0.06 accuracy for adipose tissue and muscle compartments segmentation based on Dice Similarity Coefficient. The proposed framework for muscle compartment segmentation showed an overall higher accuracy compared to ANTs and STAPLE, two previously validated atlas-based segmentation methods. Also, the framework proposed in this study showed similar Dice accuracy and better Hausdorff distance measure to that obtained using DeepMedic Convolutional Neural Network structure, a well-known deep learning network for 3-D medical image segmentation. The automatic segmentation method proposed in this study can provide fast and accurate quantification of adipose and muscle tissues, which have important health and functional implications in the SCI population

EMG denoising using GGMRF.

<p>(A) Applying GGMRF method to 2D image (B) An example of evoked potential before (black) and after (red) applying GGMRF method.</p

Novel Noninvasive Spinal Neuromodulation Strategy Facilitates Recovery of Stepping after Motor Complete Paraplegia

It has been suggested that neuroplasticity-promoting neuromodulation can restore sensory-motor pathways after spinal cord injury (SCI), reactivating the dormant locomotor neuronal circuitry. We introduce a neuro-rehabilitative approach that leverages locomotor training with multi-segmental spinal cord transcutaneous electrical stimulation (scTS). We hypothesized that scTS neuromodulates spinal networks, complementing the neuroplastic effects of locomotor training, result in a functional progression toward recovery of locomotion. We conducted a case-study to test this approach on a 27-year-old male classified as AIS A with chronic SCI. The training regimen included task-driven non-weight-bearing training (1 month) followed by weight-bearing training (2 months). Training was paired with multi-level continuous and phase-dependent scTS targeting function-specific motor pools. Results suggest a convergence of cross-lesional networks, improving kinematics during voluntary non-weight-bearing locomotor-like stepping. After weight-bearing training, coordination during stepping improved, suggesting an important role of afferent feedback in further improvement of voluntary control and reorganization of the sensory-motor brain-spinal connectome

The runtime mean and RMSE of each steps of the proposed framework.

<p>The runtime mean and RMSE of each steps of the proposed framework.</p

Schematic representation of the epidural stimulation unit (16-electrode array, IPG unit and wireless programmer) and its connections to the EMG recording system.

<p>(The Motion Lab EMG System and EMG electrodes illustrations are from Motion Lab System Inc. Manual).</p