Learning from EMG: semi-automated grading of facial nerve function

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 syste...

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Bibliographic Details
Published in:Journal of clinical monitoring and computing 2022-10, Vol.36 (5), p.1509-1517
Main Authors: Holze, Magdalena, Rensch, Leonhard, Prell, Julian, Scheller, Christian, Simmermacher, Sebastian, Scheer, Maximilian, Strauss, Christian, Rampp, Stefan
Format: Article
Language:English
Subjects:
Anesthesiology
Automation
Clinical medicine
Critical Care Medicine
Differentiation
Electromyography
Health Sciences
Intensive
Machine learning
Mathematical analysis
Measurement techniques
Medicine
Medicine & Public Health
Nerves
Original Research
Statistics for Life Sciences
Surgery
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Summary: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.
ISSN:1387-1307
1573-2614
DOI:10.1007/s10877-021-00793-y