Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 18, 2026

Polygraphic Recording Procedure for Measuring Sleep in Mice
08:45

Polygraphic Recording Procedure for Measuring Sleep in Mice

Published on: January 25, 2016

Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session

Melissa A St Hilaire1, Jason P Sullivan, Clare Anderson

  • 1Analytic and Modeling Unit, Division of Sleep Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, MA 02115, USA. msthilaire@rics.bwh.harvard.edu

Accident; Analysis and Prevention
|September 11, 2012
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Gastrointestinal malignancies in pregnancy: practical considerations.

Journal of gastrointestinal oncology·2026
Same author

Self-Reported Driving Behaviours and the Relationship With Sleepiness in Subjective Cognitive Decline and Mild Cognitive Impairment.

Journal of geriatric psychiatry and neurology·2026
Same author

Translational applications of circadian research: connecting chronobiology to medicine.

Npj biological timing and sleep·2026
Same author

Occult obstructive sleep apnea in survivors of Hodgkin lymphoma following radiation therapy: an atypical and under-recognized phenotype.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine·2026
Same author

SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks.

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies·2026
Same author

Maternal Health and Child Welfare: A Long-Overdue Conversation.

Social work·2026
Same journal

Modeling road-segment-level speeding risk of new energy vehicle taxis using a multistage framework with spatial spillover, endogeneity, and nonlinear effects.

Accident; analysis and prevention·2026
Same journal

Role of streetscape feature in pedestrian safety: A modified multi-level multiple membership model.

Accident; analysis and prevention·2026
Same journal

Assessing autonomous driving performance and environmental influencing factors using real-world operational trajectory data.

Accident; analysis and prevention·2026
Same journal

Multi-scale modeling of electric vehicle fatal crash risk: uncovering spatial heterogeneity and infrastructure-land use coupling mechanisms.

Accident; analysis and prevention·2026
Same journal

Differential sensitivity of self-reported driving and collision measures to aspects of shiftwork, sleep, and fatigue.

Accident; analysis and prevention·2026
Same journal

Delving into the visual attention of pedestrians during street crossing under time pressure: An eye-tracking approach.

Accident; analysis and prevention·2026
See all related articles

Pattern recognition algorithms show promise for detecting cognitive impairment from sleep loss using single testing sessions. These methods offer a potential new way to identify performance deficits in real-world settings.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Data Science

Background:

  • No definitive marker exists for sleep-related cognitive impairment.
  • Objective and subjective measures are needed for real-world application.

Purpose of the Study:

  • To develop and test pattern recognition algorithms for identifying cognitive performance impairment due to sleep loss.
  • To assess algorithm reliability using single-session data in both lab and field settings.

Main Methods:

  • Trained algorithms using Psychomotor Vigilance Task (PVT) and Karolinska Sleepiness Scale (KSS) data from sleep-deprived individuals.
  • Validated algorithms on separate laboratory and real-world datasets.
  • Evaluated performance based on sensitivity, specificity, and prediction accuracy.

More Related Videos

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring
04:54

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring

Published on: November 8, 2024

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Related Experiment Videos

Last Updated: May 18, 2026

Polygraphic Recording Procedure for Measuring Sleep in Mice
08:45

Polygraphic Recording Procedure for Measuring Sleep in Mice

Published on: January 25, 2016

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring
04:54

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring

Published on: November 8, 2024

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Main Results:

  • Algorithms achieved up to 82% accuracy in laboratory settings using PVT, KSS, wake duration, and time of day.
  • In real-world conditions, prediction accuracy for low impairment reached 98%.
  • Accuracy for moderate and severe impairment predictions was lower.

Conclusions:

  • Pattern recognition algorithms offer a viable approach for detecting sleep-loss-induced cognitive impairment.
  • Single-feature performance indicators from lab settings may not translate to real-world conditions.
  • Further research is needed to refine algorithms for real-world sleepiness assessments.