Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence

Interpreting Stroke-Impaired Electromyography Patterns through Explainable Artificial Intelligence

Electromyography (EMG) proves invaluable myoelectric manifestation in identifying neuromuscular alterations resulting from ischemic strokes, serving as a potential marker for diagnostics of gait impairments caused by ischemia. This study aims to develop an interpretable machine learning (ML) framewo...

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Bibliographic Details
Main Authors: Iqram Hussain, Rafsan Jany
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Sensors
Subjects:
explainable AI
electromyography
stroke
SHAP
LIME
Anchors
Online Access:https://www.mdpi.com/1424-8220/24/5/1392

Internet

https://www.mdpi.com/1424-8220/24/5/1392

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