Early detection and monitoring of Alzheimers Disease and Related Dementias using non-semantic linguistic and acoustic features of speech derived from hearing aids

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Abstract Alzheimer’s disease and related dementias (ADRD) are a serious national health concern that affected 5.8 million in 2020 and are expected to increase by 40% over the next decade. There is evidence that the functional, psychological, pathological, and physiological changes underlying ADRD may emerge many years prior to the clinical manifestation of cognitive symptoms, which is increasing the interest in early detection and monitoring to inform disease prediction and management at both the individual and population level. In addition, the higher rates of late-life depression and age-related hearing loss associated with ADRD complicate treatment over the long duration of the disease. Given the need for improved measures to understand and treat ADRD, several divisions of the National Institute of Aging have called for improved methodologies for prognosis, diagnosis and/or treatment monitoring of aging related cognitive decline that are more sensitive to early cognitive changes, less costly and noninvasive. Advances in digital health for hearing care, speech analysis and machine learning present tremendous opportunities to provide cost-effective, user-friendly cognitive measures that can be readily used, or adapted, for persons living in remote, urban, and peri-urban communities. The hearing aids (HAs) have the digital signal processing, computational and wireless communication capabilities needed for speech-analysis tasks. The unique ability of the HA for own voice detection facilitates the analysis of non-semantic paralinguistic acoustic features of speech indicative of early changes in cognitive health. The ability to extract non-semantic features of voice through the HA is a key aspect of maintaining privacy for the user outside of clinical or structured conversations, i.e. during the person’s normal activities of daily living.