Dataset Information

Identification of digital voice biomarkers for cognitive health.

ABSTRACT:

Aim

Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia.

Methods

Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted with the OpenSMILE toolkit (v2.1). The association of each acoustic feature with incident dementia was assessed by Cox proportional hazards models.

Results

Our study included 6, 528 voice recordings from 4, 849 participants (mean age 63 ± 15 years old, 54.6% women). The majority of participants (71.2%) had one voice recording, 23.9% had two voice recordings, and the remaining participants (4.9%) had three or more voice recordings. Although all asymptomatic at the time of examination, participants who developed dementia tended to have shorter segments than those who were dementia free (P < 0.001). Additionally, 14 acoustic features were significantly associated with dementia after adjusting for multiple testing (P < 0.05/48 = 1 × 10^-3). The most significant acoustic feature was jitterDDP_sma_de (P = 7.9 × 10^-7), which represents the differential frame-to-frame Jitter. A voice based linear classifier was also built that was capable of predicting incident dementia with area under curve of 0.812.

Conclusions

Multiple acoustic and linguistic features are identified that are associated with incident dementia among asymptomatic participants, which could be used to build better prediction models for passive cognitive health monitoring.

SUBMITTER: Lin H

PROVIDER: S-EPMC7929495 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Identification of digital voice biomarkers for cognitive health.

Lin Honghuang H Karjadi Cody C Ang Ting F A TFA Prajakta Joshi J McManus Chelsea C Alhanai Tuka W TW Glass James J Au Rhoda R

Exploration of medicine 20201231

<h4>Aim</h4>Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia.<h4>Methods</h4>Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted ...[more]

PMID: 33665648

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Dataset Information

Identification of digital voice biomarkers for cognitive health.

Aim

Methods

Results

Conclusions

Publications

Identification of digital voice biomarkers for cognitive health.

Similar Datasets

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