SUMMARYIn this paper, the vibration characteristics of stapes, driven by the implanted differential floating mass transducer (DFMT) in the human middle ear, are analyzed by using an electrical model. The electrical model has been simulated by using the PSpice, in which the simulated results are compared with the experimental results by using the fabricated DFMT and the human temporal bones. key words: electrical model, lumped model, middle ear, DFMT
Implantable middle ear hearing devices (IMEHDs) have been developed at several countries. Some devices can be selected rechargeable battery for long-term implant. In South Korea, a kind of IMEHD with rechargeable battery and a pillow type wireless charger have been developed. The charger includes several coils surrounding user's head in sleep. However, this device should excessively consume power due to driving all primary coils. Especially, needless exposure to magnetic field generated from uncoupled primary coils may induce biohazards. Therefore, a method reducing the body exposure to electromagnetic field was proposed as decreasing the emitted total magnetic flux during charge process. Keywords: fully implantable middle ear hearing device, wireless charger, electromagnetic coupling, electromagnetic field exposure, hall effect sensor Classification: Electronic instrumentation and control Otolaryngology Head and Neck Surgery, vol. 134, no. 6, pp. 1043-1050, 2006 [2] R. T. Ramsden, "Cochlear implants and brain stem implants, " British Medical Bulletin, vol. 63, no. 1, pp. 183-193, 2002
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Input for fully implantable hearing devices (FIHDs) is provided by an implantable microphone under the skin of the temporal bone. However, the implanted microphone can be affected when the FIHDs user chews. In this paper, a dual implantable microphone was designed that can filter out the noise from mastication. For the in vivo experiment, a fabricated microphone was implanted in a rabbit. Pure-tone sounds of 1 kHz through a standard speaker were applied to the rabbit, which was given food simultaneously. To evaluate noise reduction, the measured signals were processed using a MATLAB program based adaptive filter. To verify the proposed method, the correlation coefficients and signal to-noise ratio before and after signal processing were calculated. By comparing the results, signal-to-noise ratio and correlation coefficients are enhanced by 6.07dB and 0.529 respectively.
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