Project

MSc thesis project in data-science at Oticon Medical

Publisher

Supervisor

Location

Greater Copenhagen area

Background:

Cochlear Implants are today a very successful treatment for people with profound hearing losses. Today more than 500,000 people across the world have a CI, a number that has grown dramatically over the past decade thanks to significant improvements in CI technology.

Today deaf born children are implanted down to the age of 7months in Denmark. With a CI a deaf born child can in many cases hear well enough to participate in normal schools and can get normal jobs. Besides being a significant economic benefit to society, CI implants can significantly increase quality-of-life for deaf people or people with profound hearing losses.

One of the burden cochlear implant users must cope with is the mental exertion that often result from continuously attempting to make sense of the underspecified electrical stimuli delivered by the implant. Listening Effort is thus increasingly becoming a key performance indicator in the evaluation of success in cochlear implantation. Listening effort can be assessed subjectively: by questionnaires and analog scores, or objectively using measures such as pupil dilation during effort or heart-rate variability.

In order to better understand the links between subjective and objective measures of listening effort and their consistency between lab- and real-life evaluation, Oticon Medical has pursued a pilot clinical research study where CI users underwent a baseline laboratory assessment of listening effort in various intelligibility conditions and carried a smartphone together with a custom App enabling the collection of objective real-life data on sound, ECG-signals as well as punctual questionnaires relating to CI performance on a moment-base.

Project description:

This MSc project aims at exploiting the data acquired during this study by:

1-     Investigate whether measures objective measures of Listening Effort assessed in the lab correlate with subjective measures acquired in the field

2-     Determine whether the soundscapes experienced by the participants predict user-preferences associated with various CI sound processing strategies, and user evaluation of listening effort in the field.

3-     Conceptualize and prototype an analytics dashboard enabling the assessment of study results and anticipating the translation of the study concept to clinical audiology practice

The student will be given access to all study results: questionnaires, .sql database extracted from smartphone logs, etc. She/he will leverage relevant statistical analysis and statistical modelling techniques to try and answer questions 1 and 2. All scripts will be written in Python.

In collaboration with

Oticon Medical - François Patou

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Technical University of Denmark

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