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

> **NIH NIH R41** · HEADWATERS INNOVATION, INC. · 2022 · $265,844

## Abstract

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.

## Key facts

- **NIH application ID:** 10600233
- **Project number:** 1R41AG080977-01
- **Recipient organization:** HEADWATERS INNOVATION, INC.
- **Principal Investigator:** Brian John Bischoff
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $265,844
- **Award type:** 1
- **Project period:** 2022-09-30 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10600233

## Citation

> US National Institutes of Health, RePORTER application 10600233, Early detection and monitoring of Alzheimers Disease and Related Dementias using non-semantic linguistic and acoustic features of speech derived from hearing aids (1R41AG080977-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10600233. Licensed CC0.

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