Open AccessElectrical stimulation‐based nerve location prediction for cranial nerve VII localization in acoustic neuroma surgery
Author(s) -
Puanhvuan Dilok,
Chumnanvej Sorayouth,
Wongsawat Yodchanan
Publication year - 2018
Publication title -
brain and behavior
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.915
H-Index - 41
ISSN - 2162-3279
DOI - 10.1002/brb3.981
Subject(s) - neuroma , acoustic neuroma , biomedical engineering , sciatic nerve , stimulus (psychology) , compound muscle action potential , anatomy , medicine , nerve root , electrophysiology , surgery , psychology , psychotherapist
Cranial nerve (CN) VII localization is a critical step during acoustic neuroma surgery because the nerve is generally hidden due to the tumor mass. The patient can suffer from Bell's palsy if the nerve is accidentally damaged during tumor removal. Surgeons localize CN VII by exploring the target area with a stimulus probe. Compound muscle action potentials (CMAPs) are elicited when the probe locates the nerve. However, false positives and false negatives are possible due to unpredictable tissue impedance in the operative area. Moreover, a single CMAP amplitude is not correlated with probe‐to‐nerve distance. Objectives This paper presents a new modality for nerve localization. The probe‐to‐nerve distance is predicted by the proposed nerve location prediction model. Methods Input features are extracted from CMAP responses, tissue impedance, and stimulus current. The tissue impedance is calculated from the estimated resistance and capacitance of the tissue equivalent circuit. In this study, experiments were conducted in animals. A frog's sciatic nerve and gastrocnemius were used to represent CN VII and facial muscle in humans, respectively. Gelatin (2.8%) was used as a mock material to mimic an acoustic neuroma. The %NaCl applied to the mock material was used to emulate uncontrollable impedance of tissue in the operative area. Results The 10‐fold cross‐validation results revealed an average prediction accuracy of 86.71% and an average predicted error of 0.76 mm compared with the measurement data. Conclusion The proposed nerve location prediction model could predict the probe‐to‐nerve distance across various impedances of the mock material.