Anatomical study of the human middle ear for the design of implantable hearing aids

OBJECTIVE: To generate anatomical data on the human middle ear and adjacent structures to serve as a base for the development and optimization of new implantable hearing aid transducers. Implantable middle ear hearing aid transducers, i.e. the equivalent to the loudspeaker in conventional hearing aids, should ideally fit into the majority of adult middle ears and should utilize the limited space optimally to achieve sufficiently high maximal output levels. For several designs, more anatomical data are needed. METHODS: Twenty temporal bones of 10 formalin-fixed adult human heads were scanned by a computed tomography system (CT) using a slide thickness of 0.63 mm. Twelve landmarks were defined and 24 different distances were calculated for each temporal bone. RESULTS: A statistical description of 24 distances in the adult human middle ear which may limit or influence the design of middle ear transducers is presented. Significant inter-individual differences but no significant differences for gender, side, age or degree of pneumatization of the mastoid were found. Distances, which were not analyzed for the first time in this study, were found to be in good agreement with the results of earlier studies. CONCLUSION: A data set describing the adult human middle ear anatomy quantitatively from the point of view of designers of new implantable hearing aid transducers has been generated. In principle, the method employed in this study using standard CT scans could also be used preoperatively to rule out exclusion criteria.

Differentiating the Multipoint Expected Improvement for Optimal Batch Design

This work deals with parallel optimization of expensive objective functions which are modelled as sample realizations of Gaussian processes. The study is formalized as a Bayesian optimization problem, or continuous multi-armed bandit problem, where a batch of q > 0 arms is pulled in parallel at each iteration. Several algorithms have been developed for choosing batches by trading off exploitation and exploration. As of today, the maximum Expected Improvement (EI) and Upper Confidence Bound (UCB) selection rules appear as the most prominent approaches for batch selection. Here, we build upon recent work on the multipoint Expected Improvement criterion, for which an analytic expansion relying on Tallis’ formula was recently established. The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms. Substantial computational savings are shown in application. In addition, our algorithms are tested numerically and compared to state-of-the-art UCB-based batchsequential algorithms. Combining starting designs relying on UCB with gradient-based EI local optimization finally appears as a sound option for batch design in distributed Gaussian Process optimization.