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

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) 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:	School of Engineering and Computer Science > Institut für Optimierung und Datenanalyse IODA School of Engineering and Computer Science
Name:	Wermelinger, Jonathan; Parduzi, Qendresa; Sariyar, Murat; Raabe, Andreas; Schneider, Ulf C. and Seidel, Kathleen
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISSN:	1472-6947
Publisher:	BioMed Central
Language:	English
Submitter:	Murat Sariyar
Date Deposited:	09 Jan 2024 10:47
Last Modified:	09 Jan 2024 10:47
Publisher DOI:	10.1186/s12911-023-02276-3
ARBOR DOI:	10.24451/arbor.20860
URI:	https://arbor.bfh.ch/id/eprint/20860