AML-DECODER: Advanced Machine Learning for HD-sEMG Signal Classification-Decoding Lateral Epicondylitis in Forearm Muscles.

ABSTRACT:

Background

Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems.

Methods

We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase-amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject's label prediction.

Results

Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while st

SUBMITTER: Shirzadi M 

PROVIDER: S-EPMC11505862 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature