Low Back Pain-Induced Dynamic Trunk Muscle Control Impairments Are Associated with Altered Spatial EMG-Torque Relationships.

PURPOSE: We quantified the relationship between high-density surface electromyographic (HDsEMG) oscillations (in both time and frequency domains) and torque steadiness during submaximal concentric/eccentric trunk extension/flexion contractions, in individuals with and without chronic low back pain (CLBP). METHODS: Comparisons were made between regional differences in HDsEMG amplitude and HDsEMG-torque cross-correlation and coherence of the thoracolumbar erector spinae (ES), rectus abdominis (RA), and external oblique (EO) muscles between the two groups. HDsEMG signals were recorded from the thoracolumbar ES with two 64-electrode grids and from the RA and EO muscles with a single 64-electrode grid placed over each muscle. Torque signals were recorded with an isokinetic dynamometer. Coherence (δ band (0-5 Hz)) and cross-correlation analyses were used to examine the relationship between HDsEMG and torque signals. For this purpose, we used principal component analysis to reduce data dimensionality and improve HDsEMG-based torque estimation. RESULTS: We found that people with CLBP had poorer control during both concentric and eccentric trunk flexion and extension. Specifically, during trunk extension, they exhibited a higher HDsEMG-torque coherence in more cranial regions of the thoracolumbar ES and a higher HDsEMG cross-correlation compared with asymptomatic controls. During trunk flexion movements, they demonstrated higher HDsEMG amplitude of the abdominal muscles, with the center of activation being more cranial and a higher contribution of this musculature to the resultant torque (particularly the EO muscle). CONCLUSIONS: Our findings underscore the importance of evaluating torque steadiness in individuals with CLBP. Future research should consider the value of torque steadiness training and HDsEMG-based biofeedback for modifying trunk muscle recruitment strategies and improving torque steadiness performance in individuals with CLBP.

People with chronic low back pain display spatial alterations in high-density surface EMG-torque oscillations.

We quantified the relationship between spatial oscillations in surface electromyographic (sEMG) activity and trunk-extension torque in individuals with and without chronic low back pain (CLBP), during two submaximal isometric lumbar extension tasks at 20% and 50% of their maximal voluntary torque. High-density sEMG (HDsEMG) signals were recorded from the lumbar erector spinae (ES) with a 64-electrode grid, and torque signals were recorded with an isokinetic dynamometer. Coherence and cross-correlation analyses were applied between the filtered interference HDsEMG and torque signals for each submaximal contraction. Principal component analysis was used to reduce dimensionality of HDsEMG data and improve the HDsEMG-based torque estimation. sEMG-torque coherence was quantified in the δ(0-5 Hz) frequency bandwidth. Regional differences in sEMG-torque coherence were also evaluated by creating topographical coherence maps. sEMG-torque coherence in the δ band and sEMG-torque cross-correlation increased with the increase in torque in the controls but not in the CLBP group (p = 0.018, p = 0.030 respectively). As torque increased, the CLBP group increased sEMG-torque coherence in more cranial ES regions, while the opposite was observed for the controls (p = 0.043). Individuals with CLBP show reductions in sEMG-torque relationships possibly due to the use of compensatory strategies and regional adjustments of ES-sEMG oscillatory activity.

A machine learning approach for the identification of kinematic biomarkers of chronic neck pain during single- and dual-task gait.

BACKGROUND: Changes in gait characteristics have been reported in people with chronic neck pain (CNP). RESEARCH QUESTION: Can we classify people with and without CNP by training machine learning models with Inertial Measurement Units (IMU)-based gait kinematic data? METHODS: Eighteen asymptomatic individuals and 21 participants with CNP were recruited for the study and performed two gait trajectories, (1) linear walking with their head straight (single-task) and (2) linear walking with continuous head-rotation (dual-task). Kinematic data were recorded from three IMU sensors attached to the forehead, upper thoracic spine (T1), and lower thoracic spine (T12). Temporal and spectral features were extracted to generate the dataset for both single- and dual-task gait. To evaluate the most significant features and simultaneously reduce the dataset size, the Neighbourhood Component Analysis (NCA) method was utilized. Three supervised models were applied, including K-Nearest Neighbour, Support Vector Machine, and Linear Discriminant Analysis to test the performance of the most important temporal and spectral features. RESULTS: The performance of all classifiers increased after the implementation of NCA. The best performance was achieved by NCA-Support Vector Machine with an accuracy of 86.85%, specificity of 83.30%, and sensitivity of 92.85% during the dual-task gait using only nine features. SIGNIFICANCE: The results present a data-driven approach and machine learning-based methods to identify test conditions and features from high-dimensional data obtained during gait for the classification of people with and without CNP.

Machine learning approaches applied in spinal pain research.

The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management.

Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.

Neuromuscular impairments are frequently observed in patients with chronic neck pain (CNP). This study uniquely investigates whether changes in neck muscle synergies detected during gait are sensitive enough to differentiate between people with and without CNP. Surface electromyography (EMG) was recorded from the sternocleidomastoid, splenius capitis, and upper trapezius muscles bilaterally from 20 asymptomatic individuals and 20 people with CNP as they performed rectilinear and curvilinear gait. Intermuscular coherence was computed to generate the functional inter-muscle connectivity network, the topology of which is quantified based on a set of graph measures. Besides the functional network, spectrotemporal analysis of each EMG was used to form the feature set. With the use of Neighbourhood Component Analysis (NCA), we identified the most significant features and muscles for the classification/differentiation task conducted using K-Nearest Neighbourhood (K-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms. The NCA algorithm selected features from muscle network topology as one of the most relevant feature sets, which further emphasize the presence of major differences in muscle network topology between people with and without CNP. Curvilinear gait achieved the best classification performance through NCA-SVM based on only 16 features (accuracy: 85.00%, specificity: 81.81%, and sensitivity: 88.88%). Intermuscular muscle networks can be considered as a new sensitive tool for the classification of people with CNP. These findings further our understanding of how fundamental muscle networks are altered in people with CNP.

Kinematic biomarkers of chronic neck pain measured during gait: A data-driven classification approach.

People with chronic neck pain (CNP) often present with altered gait kinematics. This paper investigates, combines, and compares the kinematic features from linear and nonlinear walking trajectories to design supervised machine learning models which differentiate asymptomatic individuals from those with CNP. For this, 126 features were extracted from seven body segments of 20 asymptomatic subjects and 20 individuals with non-specific CNP. Neighbourhood Component Analysis (NCA) was used to identify body segments and the corresponding significant features which have the maximum discriminative power for conducting classification. We assessed the efficacy of NCA combined with K- Nearest Neighbour (K-NN), Support Vector Machine and Linear Discriminant Analysis. By applying NCA, all classifiers increased their performance for both linear and nonlinear walking trajectories. Notably, features selected by NCA which magnify the classification power of the computational model were solely from the head, trunk and pelvis kinematics. Our results revealed that the nonlinear trajectory provides the best classification performance through the NCA-K-NN algorithms with an accuracy of 90%, specificity of 100% and sensitivity of 83.3%. The selected features by NCA are introduced as key biomarkers of gait kinematics for classifying non-specific CNP. This paper provides insight into changes in gait kinematics which are present in people with non-specific CNP which can be exploited for classification purposes. The result highlights the importance of curvilinear gait kinematic features which potentially could be utilized in future research to predict recurrent episodes of neck pain.

Kinematic Biomarkers of Chronic Neck Pain During Curvilinear Walking: A Data-driven Differential Diagnosis Approach.

Chronic Neck Pain (CNP) can be associated with biomechanical changes. This paper investigates the changes in patterns of walking kinematics along a curvilinear trajectory and uses a specially designed feature space, coupled with a machine learning framework to conduct a data-driven differential diagnosis, between asymptomatic individuals and those with CNP. For this, 126 kinematic features were collected from seven body segments of 40 participants (20 asymptomatic, 20 individuals with CNP). The features space was processed through a Neighbourhood Component Analysis (NCA) algorithm to systematically select the most significant features which have the maximum discriminative power for conducting the differential diagnosis. The selected features were then processed by a K-Nearest Neighbors (K-NN) classifier to conduct the task. Our results show that, through a systematic selection of feature space, we can significantly increase the classification accuracy. In this regard, a 35% increase is reported after applying the NCA. Thus, we have shown that using only 13 features (of which 61% belong to kinematic features and 39% to statistical features) from five body segments (Head, Trunk, Pelvic, Hip and Knee) we can achieve an accuracy, sensitivity and specificity of 82.50%, 80.95% and 84.21% respectively. This promising result highlights the importance of curvilinear kinematic features through the proposed information processing pipeline for conducting differential diagnosis and could be tested in future studies to predict the likelihood of people developing recurrent neck pain.