Detection of needle to nerve contact based on electric bioimpedance and machine learning methods.

Kalvoy, Havard; Tronstad, Christian; Ullensvang, Kyrre; Steinfeldt, Thorsten; Sauter, Axel (2017). Detection of needle to nerve contact based on electric bioimpedance and machine learning methods. IEEE Engineering in Medicine and Biology Society conference proceedings, 2017, pp. 9-12. IEEE Service Center 10.1109/EMBC.2017.8036750

Full text not available from this repository. (Request a copy)

In an ongoing project for electrical impedance-based needle guidance we have previously showed in an animal model that intraneural needle positions can be detected with bioimpedance measurement. To enhance the power of this method we in this study have investigated whether an early detection of the needle only touching the nerve also is feasible. Measurement of complex impedance during needle to nerve contact was compared with needle positions in surrounding tissues in a volunteer study on 32 subjects. Classification analysis using Support-Vector Machines demonstrated that discrimination is possible, but that the sensitivity and specificity for the nerve touch algorithm not is at the same level of performance as for intra-neuralintraneural detection.

Item Type:	Journal Article (Original Article)
Division/Institute:	04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > Clinic and Policlinic for Anaesthesiology and Pain Therapy
UniBE Contributor:	Sauter, Axel
Subjects:	600 Technology > 610 Medicine & health
ISSN:	1557-170X
Publisher:	IEEE Service Center
Language:	English
Submitter:	Jeannie Wurz
Date Deposited:	26 Jan 2018 11:09
Last Modified:	05 Dec 2022 15:09
Publisher DOI:	10.1109/EMBC.2017.8036750
PubMed ID:	29059798
URI:	https://boris.unibe.ch/id/eprint/108479