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

BMC Medical Informatics and Decision Making

Research

Andreas Raabe¹  Jonathan Wermelinger¹  Kathleen Seidel¹  Ulf C. Schneider²  Qendresa Parduzi²  Murat Sariyar³ 

[1] Department of Neurosurgery, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland;Department of Neurosurgery, Lucerne Cantonal Hospital, Lucerne, Switzerland;School of Engineering and Computer Science, Bern University of Applied Sciences, Biel, Switzerland;

关键词: Machine learning;    Intraoperative neurophysiological monitoring;    Motor evoked potential;    Random forest;    Time series data;   

DOI  :  10.1186/s12911-023-02276-3

 received in 2023-06-13, accepted in 2023-08-28,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundEven 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.MethodsIntraoperative 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).ResultsIn 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).ConclusionsStandard 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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