Effective cochlear implant fitting (or programming) is essential for providing good hearing outcomes, yet it is a subjective and error-prone task. The initial objective of this research was to automate the procedure using the auditory nerve electrically evoked compound action potential (the ECAP) and machine intelligence. The Nucleus?? cochlear implant measures the ECAP via its Neural Response Telemetry (NRT) system. AutoNRT, a commercial intelligent system that measures ECAP thresholds with the Nucleus Freedom implant, was firstly developed in this research. AutoNRT uses decision tree expert systems that automatically recognise ECAPs. The algorithm approaches threshold from lower stimulus levels, ensuring recipient safety during postoperative measurements. Clinical studies have demonstrated success on approximately 95% of electrodes, measured with the same efficacy as a human expert. NRT features other than ECAP threshold, such as the ECAP recovery function, could not be measured with similar success rates, precluding further automation and loudness prediction from data mining results. Despite this outcome, a better application of the ECAP threshold profile towards fitting was established. Since C-level profiles (the contour of maximum acceptable stimulus levels across the implant array) were observed to be flatter than T-level profiles (the contour of minimum audibility), a flattening of the ECAP threshold profile was adopted when applied as a fitting profile at higher stimulus levels. Clinical benefits of this profile scaling technique were demonstrated in a 42 subject study. Data mining results also provided an insight into the ECAP recovery function and refractoriness. It is argued that the ECAP recovery function is heavily influenced by the size of the recruited neural population, with evidence gathered from a computational model of the cat auditory nerve and NRT measurements with 21 human subjects. Slower ECAP recovery, at equal loudness, is a consequence of greater neural recruitment leading to lower mean spike probabilities. This view can explain the counterintuitive association between slower ECAP recovery and greater temporal responsiveness to increasing stimulation rate. This thesis presents the first attempt at achieving completely automated cochlear implant fitting via machine intelligence; a future generation implant, capable of high fidelity auditory system measurements, may realise the ultimate objective.
| Identifer | oai:union.ndltd.org:ADTP/272660 |
| Date | January 2010 |
| Creators | Botros, Andrew, Computer Science & Engineering, Faculty of Engineering, UNSW |
| Publisher | Awarded By:University of New South Wales. Computer Science & Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://unsworks.unsw.edu.au/copyright, http://unsworks.unsw.edu.au/copyright |
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