Abstract

Abstract Cochlear implants are hearing prostheses for patients with severe to total hearing loss but intact auditory nerve. An external speech processor captures sound from the environment, which is subsequently converted into electrical signals and transmitted to an electrode array in the patient’s inner ear. The metallic stimulation electrodes of the electrode array electrically stimulate the spiral ganglion cells of the auditory nerve. The functionality of cochlear implants strongly depends on the possible maximum current stimulating the spiral ganglion cells, which can be affected by, e.g., cell growth around the stimulation electrodes. This in turn decreases the stimulation efficiency leading to decreased hearing. Cell growth, implant position and other changes in the surrounding medium are reflected in a change of the impedance of the stimulation electrodes. The impedance measurement of the stimulation electrodes is already implemented in all common cochlear implant systems to check functionality of the stimulation electrodes after implantation, but the frequency spectrum is normally not analyzed. Although this method can detect cell growth on the stimulation electrodes, it faces limitations when other interfering effects, such as changes in the perilymph and implant position, influence the impedance. This work shows impedance spectroscopic measurements using enlarged cochlear implant models to electrically analyze the surrounding medium, the perilymph, to understand changes in electrode impedance and to later monitor the stimulation efficiency of cochlear implants and to identify possible reasons for decreased hearing ability by impedance spectroscopy. In addition, we use FEM simulations to numerically model the influence of the perilymph composition on the impedance measurement. As shown by a final validation, this model can serve as a basis for an extended simulation model including implant position and cell growth monitoring to predict hearing deterioration in cochlear implant patients. In this context, this work serves as a basis for the development of a holistic prediction model and considers in the first step exclusively the influence of the perilymph composition on the impedance between two stimulation electrodes.

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