Effects of a particle placed on the ossicles for microphoneless cochlear implant design.

In a typical cochlear implant design, the ambient sound is detected via a microphone and the transmission unit of the implant is placed at the back of the auricle. However, this design has several drawbacks. Firstly, the subject cannot bath or swim comfortably with the microphone unit on, and secondly having an external attached unit which may be visible is cosmetically disturbing. Herein, the idea is to explore obtaining the acoustic signals that would directly drive the cochlear nerves, without using a microphone, in which only the vibrations of the ossicles are employed. Thus, the natural filter caused by the anatomy of the ear may be maintained. The proposed method is to place or attach a micro-electro-mechanical-system (MEMS) type of tiny and lightweight accelerometer to sense or detect the vibrations of ossicles, namely malleus, incus and stapes. A quick analysis or first-thought revealed that physically longer extension of the incus is the most suitable and/or convenient place to attach such a sensor. The model adopted has been optimized to match the amplitude and phase response of the human ear from a system analysis point of view. Some simulation experiments had been done to study and understand the possible loading effects of placing a sensor on the incus. Purpose of the simulations is testing the feasibility before the very difficult surgical procedures. Preliminary results indicate that placing a sensor of weight up to 36 mg does not seriously affect the amplitude and the phase response of the ear. This study is yet another example of how simulations of physiological systems can be advantageous and facilitating in the design of biomedical systems.

Influence of Time and Direction Information on Video Head Impulse Gains.

OBJECTIVE: The objective of this study was to establish which factor leads to a higher vestibulo-ocular reflex (VOR) gain: the timing of the movement or the direction of the movement. For this purpose, healthy volunteers were examined under three conditions: (1) when they were informed about the timing of the head movement; (2) when they were informed about the direction of the head movement; and (3) when they knew both the timing and the direction of the head movement. MATERIALS AND METHODS: This study included data from 19 participants between the ages of 20 and 23 years with no neurological or vestibular ailments. The gains of the video head impulse test (vHIT) were measured under four different conditions and the final control tests. Five subgroups were defined, and the differences in the subgroups were assessed with using several statistical procedures. RESULTS: We found that there were significant differences between all subgroups gains on the right and left head rotations. Nevertheless, nonsignificant differences were found by performing independent samples t-tests and Mann-Whitney U tests between left and right head rotations for the pairwise comparisons of subgroups. Also, analysis of variance (ANOVA) results indicated that vHIT gains for the right and left , respectively). Thus, knowing the timing or direction or both does not affect vHIT gains. CONCLUSION: The results of these experiments revealed that there is no association whatsoever between VOR gain and awareness of the timing or direction of the movement or both.

Diagnosis of renal failure disease using Adaptive Neuro-Fuzzy Inference System.

Adaptive Neuro-Fuzzy Inference System (ANFIS) is one of the useful and powerful neural network approaches for the solution of function approximation and pattern recognition problems in the last decades. In this paper, the diagnosis of renal failure disease is investigated using ANFIS approach. Totally the raw data of 112 patients is obtained from Istanbul and Cerrahpasa Medical Faculties of Istanbul University, Turkey. Sixty-four of them are related to renal failures and the rest data belong to healthy persons. In ANFIS model, three rules and Gaussian membership functions are chosen, where rules are determined by the subtractive clustering method. Seven parameters of the patients are considered for the input of the system. These are: Blood Urea Nitrogen (BUN), Creatinine, Uric Acid, Potassium (K), Calcium (Ca), Phosphorus (P) and age. We try to decide whether the patient is ill or not. We have reached 100% success in ANFIS and have better results compared to Support Vector Machine (SVM) and Artificial Neural Networks (ANN).