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Automatic detection and classification of the Auditory Brainstem Responses (ABR) is used in newborn hearing screening. Improved detection algorithms will reduce test time, prevent infants with hearing loss from being missed while reducing the number of normal hearing babies referred to diagnostic testing. We have already improved the objectivity of ABR classification in neurological assessments by using Continuous Wavelet Transform (CWT) and Machine Learning (ML). In the proposed project, we seek to validate our findings further to improve the objectivity in the newborn hearing assessment. We intend to implement the algorithm in real-time for faster and accurate diagnosis of hearing impairment. The project will be carried in partnership with Vivosonic Inc.; a reputable company focused on auditory screening and diagnostics. It is expected that the outcome of this work will be beneficial to the partner to improve their system to supply clinicians with valuable clinical tools.
Engineering - computer / electrical
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