Opportunities and challenges of supervised machine learning for the classification of motor evoked potentials according to muscles.

ABSTRACT:

Background

Even for an experienced neurophysiologist, it is challenging to look at a single graph of an unlabeled motor evoked potential (MEP) and identify the corresponding muscle. We demonstrate that supervised machine learning (ML) can successfully perform this task.

Methods

Intraoperative MEP data from supratentorial surgery on 36 patients was included for the classification task with 4 muscles: Extensor digitorum (EXT), abductor pollicis brevis (APB), tibialis anterior (TA) and abductor hallucis (AH). Three different supervised ML classifiers (random forest (RF), k-nearest neighbors (kNN) and logistic regression (LogReg)) were trained and tested on either raw or compressed data. Patient data was classified considering either all 4 muscles simultaneously, 2 muscles withi

SUBMITTER: Wermelinger J 

PROVIDER: S-EPMC10544622 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature