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Updated: Jul 1, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Published on: July 14, 2023

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Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence.

Iqram Hussain1, Rafsan Jany2

  • 1Department of Anesthesiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary
This summary is machine-generated.

This study developed an interpretable machine learning model using electromyography (EMG) to detect stroke-related gait impairments. The model accurately distinguishes stroke patients from healthy individuals, offering a new tool for rehabilitation.

Keywords:
AnchorsLIMESHAPelectromyographyexplainable AIstroke

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Area of Science:

  • Biomedical Engineering
  • Neurology
  • Machine Learning

Background:

  • Ischemic stroke often leads to gait impairments, impacting patient recovery and quality of life.
  • Electromyography (EMG) provides valuable insights into neuromuscular function, potentially serving as a diagnostic marker for stroke-induced gait issues.
  • Current diagnostic methods may not fully capture the subtle neuromuscular changes affecting gait post-stroke.

Purpose of the Study:

  • To develop an interpretable machine learning (ML) framework using EMG signals to differentiate between stroke patients and healthy individuals.
  • To identify key EMG features indicative of stroke-related gait impairments through Explainable Artificial Intelligence (XAI) techniques.
  • To establish an objective tool for predicting and managing post-stroke gait dysfunction.

Main Methods:

  • Collected EMG data from 48 stroke patients and 75 healthy adults during walking in a gait laboratory.
  • Utilized wearable sensors on the bicep femoris and lateral gastrocnemius muscles of both lower limbs.
  • Employed boosting ML models (e.g., GBoost) and XAI techniques (SHAP, LIME, Anchors) for classification and interpretation.

Main Results:

  • The GBoost model achieved high classification performance, with an AUROC of 0.94 on the training set and 0.92 on the testing set (85.26% accuracy).
  • XAI analyses identified specific EMG spectral features from the right bicep femoris and lateral gastrocnemius muscles as crucial for distinguishing stroke patients.
  • The interpretable model effectively highlighted the neuromuscular alterations associated with stroke-related gait impairments.

Conclusions:

  • An interpretable EMG-based ML model can accurately predict stroke-related gait impairments.
  • This approach offers a promising, objective tool for early detection and personalized rehabilitation strategies post-stroke.
  • The identified EMG biomarkers can significantly aid in managing gait dysfunction in stroke survivors.