Phase 2- A collaboration between the University of Ottawa and Vivosonic towards the use of the Integrity V500 in the quantitative evaluation of tinnitus

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Tinnitus is a life-altering condition that affects 360,000 Canadians and represents an economic burden of**hundreds of millions to the Canadian economy. While subjective tools are currently used to diagnose the**condition, no reliable clinical objective measure exists. The purpose of this Engage Plus is to develop the**hardware, algorithms and testing protocol required to discriminate tinnitus from the normal hearing population**with our partner, Vivosonic Inc. Our success will lead to an innovative clinical measurement tool.**Our research team has developed an innovative gap-in-noise technique that could theoretically be applied to**humans for tinnitus detection. Vivosonic Inc manufacturers the Integrity(TM) V500 system which records**electrical responses from the auditory nerve to the brainstem, known as the auditory brainstem response (ABR).**Their technology is unique on the market as it uses a patented algorithm, pre-filter, onsite amplification, and a**digital transmission that, together, minimize the level of noise captured in the recording. In our Phase-1 Engage**we created a prototype and temporal processing test that was piloted on a normal population. We confirmed**that their platform is ideal for application of our gap-in-noise protocol and capturing small ABR changes under**noisy (clinically relevant) conditions. These two factors are critical for developing a clinically viable**measurement system.**In this Engage Plus our goal is to introduce modifications to the hardware, temporal analysis and collection**protocol required to classify the tinnitus population. We will use a noise stimulus with small silent intervals of**altering durations to measure the response of the auditory nerve, modifying stimulus frequency and gap width**ranges during the protocol. The modified system will extract our novel ABR measures, these will be treated**with Vivosonic's proprietary signal conditioning and machine learning based classification approach to...