SBIR-STTR Award

Early detection and monitoring of Alzheimers Disease and Related Dementias using non-semantic linguistic and acoustic features of speech derived from hearing aids
Award last edited on: 3/15/2023

Sponsored Program
STTR
Awarding Agency
NIH : NIA
Total Award Amount
$265,844
Award Phase
1
Solicitation Topic Code
866
Principal Investigator
Brian John Bischoff

Company Information

Headwaters Innovation Inc (AKA: Bischoff Holdings Inc~Red Wing)

8313 Delaney Circle
Inver Grove Heights, MN 55076
   (612) 802-0219
   N/A
   www.hwinnovate.com

Research Institution

Boston University

Phase I

Contract Number: 1R41AG080977-01
Start Date: 9/30/2022    Completed: 8/31/2023
Phase I year
2022
Phase I Amount
$265,844
Alzheimer's disease and related dementias (ADRD) are a serious national healthconcern that affected 5.8 million in 2020 and are expected to increase by 40% over thenext decade. There is evidence that the functional, psychological, pathological, andphysiological changes underlying ADRD may emerge many years prior to the clinicalmanifestation of cognitive symptoms, which is increasing the interest in early detectionand monitoring to inform disease prediction and management at both the individual andpopulation level. In addition, the higher rates of late-life depression and age-relatedhearing loss associated with ADRD complicate treatment over the long duration of thedisease. Given the need for improved measures to understand and treat ADRD, severaldivisions of the National Institute of Aging have called for improved methodologies forprognosis, diagnosis and/or treatment monitoring of aging related cognitive decline thatare more sensitive to early cognitive changes, less costly and noninvasive.Advances in digital health for hearing care, speech analysis and machine learningpresent tremendous opportunities to provide cost-effective, user-friendly cognitivemeasures that can be readily used, or adapted, for persons living in remote, urban, andperi-urban communities. The hearing aids (HAs) have the digital signal processing,computational and wireless communication capabilities needed for speech-analysistasks. The unique ability of the HA for own voice detection facilitates the analysis ofnon-semantic paralinguistic acoustic features of speech indicative of early changes incognitive health. The ability to extract non-semantic features of voice through the HA is akey aspect of maintaining privacy for the user outside of clinical or structuredconversations, i.e. during the person's normal activities of daily living.

Public Health Relevance Statement:
Narrative 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.

Project Terms:

Phase II

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Start Date: 00/00/00    Completed: 00/00/00
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