Current research projects at the IHCE

Cochlear implants (CIs) restore hearing in individuals with sensorineural hearing loss by bypassing most of the auditory chain and directly stimulating the hearing nerve with electric charges. The required charge varies across users and electrode channels, necessitating individual adjustment of CI parameters to optimize hearing. The Maximal Comfortable Loudness Level (MCL) is the key parameter for programming. This fitting process is lengthy and relies on subjective user feedback, posing challenges for children and individuals with communication impairments, increasing the risk of over- or under-stimulation. Objectifying CI fitting is essential to improve reliability, efficiency, and overall quality of life for CI users. This research develops AI-based methods using machine/deep learning to automate sound-evoked biosignal analysis, focusing on acoustic stapedius reflex detection. As it correlates with the Maximal Comfortable Loudness Level (MCL), it serves as an objective auditory response measure. A study with CI users measured the Electrically Evoked Stapedius Reflex Threshold (eSRT) to refine these methods. The goal is to automate CI fitting, reducing time, improving accuracy, and minimizing hospital visits. This could significantly enhance CI users’ quality of life through more precise adjustments. In collaboration with Graz University of Technology, the Institute of Health Care Engineering, and clinical partners (e.g., AKH University Hospital Wien, Tübingen University Hospital), the project runs from June 1, 2025, to May 31, 2028.