Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Predicting subjective sleepiness during auditory cognitive testing using voice signaling analysis
by
Te, Tue T.
, Naeim, Arash
, Kremen, Sarah
, Bui, Alex A. T.
, Alessi, Cathy
, Martin, Jennifer L.
, Dzierzewski, Joseph M.
, Fung, Constance H.
, Boland, Mary Regina
, Ghadimi, Sara
in
Cognition & reasoning
/ Cognitive ability
/ Episodic memory
/ Insomnia
/ Machine learning
/ Medicine
/ Medicine & Public Health
/ Neurology
/ Older people
/ Pneumology/Respiratory System
/ Self report
/ Semantics
/ Sleep deprivation
/ Sleep disorders
/ Smartphones
2025
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Predicting subjective sleepiness during auditory cognitive testing using voice signaling analysis
by
Te, Tue T.
, Naeim, Arash
, Kremen, Sarah
, Bui, Alex A. T.
, Alessi, Cathy
, Martin, Jennifer L.
, Dzierzewski, Joseph M.
, Fung, Constance H.
, Boland, Mary Regina
, Ghadimi, Sara
in
Cognition & reasoning
/ Cognitive ability
/ Episodic memory
/ Insomnia
/ Machine learning
/ Medicine
/ Medicine & Public Health
/ Neurology
/ Older people
/ Pneumology/Respiratory System
/ Self report
/ Semantics
/ Sleep deprivation
/ Sleep disorders
/ Smartphones
2025
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Predicting subjective sleepiness during auditory cognitive testing using voice signaling analysis
by
Te, Tue T.
, Naeim, Arash
, Kremen, Sarah
, Bui, Alex A. T.
, Alessi, Cathy
, Martin, Jennifer L.
, Dzierzewski, Joseph M.
, Fung, Constance H.
, Boland, Mary Regina
, Ghadimi, Sara
in
Cognition & reasoning
/ Cognitive ability
/ Episodic memory
/ Insomnia
/ Machine learning
/ Medicine
/ Medicine & Public Health
/ Neurology
/ Older people
/ Pneumology/Respiratory System
/ Self report
/ Semantics
/ Sleep deprivation
/ Sleep disorders
/ Smartphones
2025
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Predicting subjective sleepiness during auditory cognitive testing using voice signaling analysis
Journal Article
Predicting subjective sleepiness during auditory cognitive testing using voice signaling analysis
2025
Request Book From Autostore
and Choose the Collection Method
Overview
Background
To determine whether objective markers of sleepiness can be collected passively using voice data to detect sleepiness in individuals undergoing testing in situations where sleepiness is not the focal point of assessment. We assessed verbal reaction time (VRT) as a vocalic marker of subjective sleepiness in middle aged and older adults with history of insomnia and benzodiazepine-receptor-agonist (BZRA) use.
Methods
Adults aged ≥55 without a diagnosis of dementia were recruited from a BZRA deprescribing clinical trial and enrolled in the present study that tested the feasibility of cognitive testing using out-of-office, self-directed mobile apps. Participants’ working/episodic memory were assessed through recorded verbal responses to Verbal Paired Associates (VPA) tests, and ecological momentary assessments (EMA) of self-reported sleepiness (1[not at all] to 4[more prominent]). Using a generalized additive model, we examined the association between VRT during VPA testing and self-reported sleepiness, adjusting for demographic, test parameters, caffeine intake, cognition, mood, and BZRA-use (
p
≤0.05 was considered significant). A stratified k-fold cross-validation/random forest (SKCV/RF) was performed to classify sleepiness levels, adjusting for other variables.
Results
We analyzed 1,513 observations from 16 patients. VRT was operationalized as the time duration between recording start time and first speech epoch. Longer VRTs were positively associated with greater EMA sleepiness (
p
≤0.05). The SKCV/RF model yielded a mean F1-score of 0.80 ± 0.08 across folds.
Conclusions
Longer VRTs correlated with greater self-reported sleepiness, indicating that voice data can be used as a marker of sleepiness in patients undergoing cognitive testing in out-of-office settings.
Publisher
BioMed Central,Springer Nature B.V,BMC
This website uses cookies to ensure you get the best experience on our website.