Opportunities and challenges of supervised machine learning for the classification of motor evoked potentials according to muscles.
BMC Med Inform Decis Mak
; 23(1): 198, 2023 10 02.
Article
em En
| MEDLINE
| ID: mdl-37784044
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 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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Músculo Esquelético
/
Potencial Evocado Motor
Limite:
Humans
Idioma:
En
Revista:
BMC Med Inform Decis Mak
Ano de publicação:
2023
Tipo de documento:
Article