| Abstract |
During my talk I will outline a brand new project (start date:
01.11.2010!) that aims at developing an intelligent, self-learning
tuning module for semi-automated fitting of cochlear implants (look
below for more details). The CI-group will play a key role in this
multi-national, multi-disciplinary challenging (both from academic and
business perspective) R&D activity, sponsored by the EU within the 7th
Framework Programme.
=== Longer Descritpion===
OPTI-FOX: OPTImization of the automated Fitting to Outcomes eXpert with
language-independent hearing-in-noise test battery and
electro-acoustical test box for cochlear implant users.
Patients suffering from sensori-neural hearing loss caused by damaged
hair cells in the cochlea and diagnosed as being profoundly deaf, are
potential candidates for cochlear implantation. Today, there are a
number of important limitations with respect to the optimal use of these
devices in deaf patients. Firstly, cochlear implant speech processors
need to be adjusted so that sounds perceived by the patient are
representational and at a comfortable level. Manual fitting, currently
the norm, is technically demanding and time consuming and clearly
suboptimal, as it involves only two of the many electrical parameters in
the speech processor. Secondly, manipulation of the implant settings is
based on subjective judgements of the patient, which are often
inconsistent and do not reflect the outcomes on psychoacoustic measures.
For the last few years, experts in the field have expressed the need for
a new fitting process that optimizes the patient's hearing in a more
efficient and accurate way. For this to happen, the fitting procedure
should change from a comfort-driven approach to an outcome-driven one.
It should also address as many electrical parameters as possible.
Ideally, a cochlear implant should come with an assisted or
(semi-)automated fitting procedure in which a large number of parameters
may be adjusted, based on measured psycho-acoustic feedback from the
implant user. Such an assisted fitting process would drastically reduce
the number of man-hours of fitting during the lifetime of the device
with qualitative with qualitatively better outcomes.
The main objectives of the proposed research project are therefore (i)
to turn an existing theoretical automated fitting model into a clinical
application by means of various techniques from statistics, machine
learning and optimisation; (ii) to develop an evaluation tool to measure
functional hearing capacities, in casu the ability to understand
speech-in-noise, representative for day-to-day listening situations.
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