Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury

Scientific Reports, 2019 · DOI: 10.1038/s41598-019-50938-y · Published: October 14, 2019

Simple Explanation

This research introduces a new method for analyzing EMG data to understand how epidural stimulation helps people with spinal cord injuries stand. The study found that specific patterns of muscle activity, identified through spectral analysis, are linked to independent standing. The developed algorithm can assess the effectiveness of muscle activation during standing, potentially aiding in the selection of optimal stimulation parameters.

Study Duration

Not specified

Participants

Eleven motor complete research participants

Evidence Level

Not specified

Key Findings

1
Independent standing is associated with EMG activity characterized by lower median frequency, lower variability of median frequency, lower variability of activation pattern, lower variability of instantaneous maximum power, and higher total power.
2
Continuous Wavelet Transform (CWT) is more effective than Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) for identifying EMG features that characterize independent standing.
3
A machine learning algorithm integrating CWT-derived features and time-domain EMG features can accurately classify assisted versus independent standing with high accuracy (92.2% to 97.5%).

Research Summary

This study introduces a novel framework for EMG data processing that uses spectral analysis and machine learning to characterize EMG activity during standing in individuals with spinal cord injury. The research identifies specific EMG features, including lower median frequency and higher total power, associated with independent standing and demonstrates that CWT is a superior method for analyzing EMG spectral features in this context. The study develops a machine learning algorithm that can accurately classify assisted versus independent standing and rank the effectiveness of muscle activation patterns, potentially aiding in the selection of optimal stimulation parameters for standing motor rehabilitation.

Practical Implications

Personalized Stimulation

The framework can assist in selecting individual-specific spinal cord epidural stimulation (scES) parameters to improve standing ability.

Rehabilitation Optimization

The algorithm can provide feedback on the effectiveness of muscle-specific activation for standing, helping clinicians optimize rehabilitation strategies.

Clinical Translation

The findings may contribute to facilitating the clinical translation of scES for standing motor rehabilitation.

Study Limitations

1
The study was not designed to investigate how different stimulation parameters and activity-based training contributed to the activation pattern characteristics resulting in assisted or independent standing.
2
The framework does not sufficiently discriminate between standing conditions with hips assisted and hips independent while the knees maintain independent extension.
3
Trunk muscles contributing to hip joint control were not considered for analysis because of the presence of scES artifacts.

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