Wermelinger, Jonathan; Parduzi, Qendresa; Sariyar, Murat; Raabe, Andreas; Schneider, Ulf C; Seidel, Kathleen (2023). Opportunities and challenges of supervised machine learning for the classification of motor evoked potentials according to muscles. BMC medical informatics and decision making, 23(1), p. 198. BioMed Central 10.1186/s12911-023-02276-3
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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 within the same extremity (EXT versus APB), or 2 muscles from different extremities (EXT versus TA).
RESULTS
In all cases, RF classifiers performed best and kNN second best. The highest performances were achieved on raw data (4 muscles 83%, EXT versus APB 89%, EXT versus TA 97% accuracy).
CONCLUSIONS
Standard ML methods show surprisingly high performance on a classification task with intraoperative MEP signals. This study illustrates the power and challenges of standard ML algorithms when handling intraoperative signals and may lead to intraoperative safety improvements.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurosurgery |
UniBE Contributor: |
Wermelinger, Jonathan, Raabe, Andreas, Seidel, Kathleen |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1472-6947 |
Publisher: |
BioMed Central |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
03 Oct 2023 16:05 |
Last Modified: |
04 Oct 2023 12:22 |
Publisher DOI: |
10.1186/s12911-023-02276-3 |
PubMed ID: |
37784044 |
Uncontrolled Keywords: |
Intraoperative neurophysiological monitoring Machine learning Motor evoked potential Random forest Time series data |
BORIS DOI: |
10.48350/186866 |
URI: |
https://boris.unibe.ch/id/eprint/186866 |
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