Learning from EMG: semi-automated grading of facial nerve function.
J Clin Monit Comput
; 36(5): 1509-1517, 2022 10.
Article
en En
| MEDLINE
| ID: mdl-34989949
ABSTRACT
The current grading of facial nerve function is based on subjective impression with the established assessment scale of House and Brackmann (HB). Especially for research a more objective method is needed to lower the interobserver variability to a minimum. We developed a semi-automated grading system based on (facial) surface EMG-data measuring the facial nerve function of 28 patients with vestibular schwannoma surgery. The sEMG was recorded preoperatively, postoperatively and after 3-12 months. In addition, the HB grade was determined. After manual selection and preprocessing, the data were subjected to machine learning classificators (Logistic regression, SVM and KNN). Lateralization indices were calculated and multivariant machine learning analysis was performed according to three scenarios [differentiation of normal (1) and slight (2) vs. impaired facial nerve function and classification of HB 1-3 (3)]. The calculated AUC for each scenario showed overall good differentiation capability with a median AUC of 0.72 for scenario 1, 0.91 for scenario 2 and multiclass AUC of 0.74 for scenario 3. This study approach using sEMG and machine learning shows feasibility regarding facial nerve grading in perioperative VS-surgery setting. sEMG may be a viable alternative to House Brackmann regarding objective evaluation of facial function especially for research purposes.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neuroma Acústico
/
Nervio Facial
Tipo de estudio:
Observational_studies
Límite:
Humans
Idioma:
En
Revista:
J Clin Monit Comput
Asunto de la revista:
INFORMATICA MEDICA
/
MEDICINA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Alemania