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 Concept Videos

Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this principle...

You might also read

Related Articles

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

Sort by
Same author

Technical and Clinical Validation of a Portable Optical Fibre Balance Mat for Quantifying Postural Sway in Older Adults.

Sensors (Basel, Switzerland)·2026
Same author

Connectivity-Based Pain Recognition from fNIRS: Parsimonious Subject-Independent Classification.

Sensors (Basel, Switzerland)·2026
Same author

Real-Time Pain Assessment from Electrodermal Activity Using Deep Learning.

Sensors (Basel, Switzerland)·2026
Same author

A systematic review and meta-analysis on dual-task sensor-based motion analysis for dementia detection.

Frontiers in digital health·2026
Same author

Sensor-based motion analysis for dementia detection: a systematic review.

Frontiers in digital health·2026
Same author

Exploring Prefrontal Cortex Involvement in Postural Control Across Degraded Sensory Conditions Using fNIRS and Classification.

IEEE journal of biomedical and health informatics·2025

Related Experiment Video

Updated: May 28, 2026

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram
09:00

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram

Published on: July 7, 2023

3.5K

Multilevel Pain Assessment with Functional Near-Infrared Spectroscopy: Evaluating ΔHBO2 and ΔHHB Measures for

Muhammad Umar Khan1, Maryam Sousani1, Niraj Hirachan1

  • 1Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT 2617, Australia.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary

This study introduces a novel method for objective pain assessment in non-verbal patients using functional near-infrared spectroscopy (fNIRS) and machine learning. Combining oxygenated and deoxygenated hemoglobin signals achieved 68.51% accuracy in classifying pain levels.

Keywords:
SVMfNIRSmachine learningpain assessmentstatistical features

More Related Videos

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.2K
Author Spotlight: Enhancing Vascular Function and Physical Capacity in Cardiovascular Disease Through Novel Interventions and NIRS Technology
04:44

Author Spotlight: Enhancing Vascular Function and Physical Capacity in Cardiovascular Disease Through Novel Interventions and NIRS Technology

Published on: March 22, 2024

953

Related Experiment Videos

Last Updated: May 28, 2026

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram
09:00

Author Spotlight: Quantifying Pain Experience – An Illustrative Approach Using the Pain Body Diagram

Published on: July 7, 2023

3.5K
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.2K
Author Spotlight: Enhancing Vascular Function and Physical Capacity in Cardiovascular Disease Through Novel Interventions and NIRS Technology
04:44

Author Spotlight: Enhancing Vascular Function and Physical Capacity in Cardiovascular Disease Through Novel Interventions and NIRS Technology

Published on: March 22, 2024

953

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Medical Informatics

Background:

  • Assessing pain in non-verbal patients relies heavily on subjective clinical judgment, which is often unreliable.
  • Objective diagnostic tools for pain assessment are lacking, particularly for critically-ill patients or those with advanced dementia.
  • Neurophysiological signals, such as fNIRS and EEG, offer insights into brain activity related to pain processing.

Purpose of the Study:

  • To develop and evaluate an objective pain assessment method using functional near-infrared spectroscopy (fNIRS) signals and machine learning.
  • To investigate the utility of oxygenated hemoglobin (ΔHBO2) and deoxygenated hemoglobin (ΔHHB) measures from fNIRS for pain detection.
  • To explore feature-level fusion of fNIRS data for improved pain classification accuracy.

Main Methods:

  • Utilized 24-channel fNIRS data, preprocessing to remove artifacts and noise.
  • Extracted ten statistical features from ΔHBO2 and ΔHHB signals, fusing them into a single vector.
  • Applied Minimum Redundancy Maximum Relevance for feature selection, followed by Support Vector Machines classification.
  • Employed leave-one-subject-out cross-validation for performance evaluation.

Main Results:

  • Achieved a classification accuracy of 68.51%±9.02% for a three-class pain assessment (No Pain, Low Pain, High Pain).
  • The fusion of ΔHBO2 and ΔHHB measures demonstrated superior performance compared to using either measure independently.
  • Successfully identified relevant features for pain classification using the MRMR method.

Conclusions:

  • The developed fNIRS-based machine learning approach shows promise for objective pain assessment in non-verbal individuals.
  • Combined ΔHBO2 and ΔHHB signals serve as a potential objective biomarker for human pain.
  • Further research can refine this method for clinical application in challenging patient populations.