Title
Automatic electrode configuration selection for image-guided cochlear implant programming
Abstract
Cochlear implants (CIs) are neural prosthetics that stimulate the auditory nerve pathways within the cochlea using an implanted electrode array to restore hearing. After implantation, the CI is programmed by an audiologist who determines which electrodes are active, i.e., the electrode configuration, and selects other stimulation settings. Recent clinical studies by our group have shown that hearing outcomes can be significantly improved by using an image-guided electrode configuration selection technique we have designed. Our goal in this work is to automate the electrode configuration selection step with the long term goal of developing a fully automatic system that can be translated to the clinic. Until now, the electrode configuration selection step has been performed by an expert with the assistance of image analysis-based estimates of the electrode-neural interface. To automatically determine the electrode configuration, we have designed an optimization approach and propose the use of a cost function with feature terms designed to interpret the image analysis data in a similar fashion as the expert. Further, we have designed an approach to select parameters in the cost function using our database of existing electrode configuration plans as training data. The results we present show that our automatic approach results in electrode configurations that are better or equally as good as manually selected configurations in over 80% of the cases tested. This method represents a crucial step towards clinical translation of our image-guided cochlear implant programming system.
Year
DOI
Venue
2015
10.1117/12.2081473
Proceedings of SPIE
Keywords
Field
DocType
Cochlear implant,stimulation strategy,image-guided cochlear implant programming
Training set,Neuroprosthetics,Computer vision,Electrode array,Cochlear implant,Artificial intelligence,Audiologist,Computer hardware,Electrode,Computer programming,Physics
Conference
Volume
ISSN
Citations 
9415
0277-786X
1
PageRank 
References 
Authors
0.41
0
3
Name
Order
Citations
PageRank
Yiyuan Zhao1154.53
Benoit M. Dawant21388223.11
Jack H. Noble313930.87