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

Applications of Stress01:04

Applications of Stress

403
Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
The...
403
Classification of Signals01:30

Classification of Signals

894
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
894

You might also read

Related Articles

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

Sort by
Same authorSame journal

An AI-driven deep learning pipeline for taxonomic classification and biodiversity assessment of deep-sea environmental DNA.

Computers in biology and medicine·2026
Same author

Earlier prediction of Parkinson's disease using cross non-decimated wavelet transform and machine learning algorithm.

Scientific reports·2025
Same author

Recognition of Parkinson disease using Kriging Empirical Mode Decomposition via deep learning techniques.

Gait & posture·2025
Same author

An integrated approach for mental health assessment using emotion analysis and scales.

Healthcare technology letters·2025
Same author

Cardiac disease risk prediction using machine learning algorithms.

Healthcare technology letters·2024
Same author

Classification of SSVEP-EEG signals using CNN and Red Fox Optimization for BCI applications.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2022
Same journal

sEEGnal: an automated EEG preprocessing pipeline evaluated against expert-driven preprocessing.

Computers in biology and medicine·2026
Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026
Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026
Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026
Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.6K

An effective multi-modality analysis for stress classification: A signal-to-image conversion using local pattern

L Susmitha1, A Shamila Ebenezer2, S Jeba Priya1

  • 1School of Computer Science and Technology, Karunya Institute of Technology and Sciences, India.

Computers in Biology and Medicine
|August 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel pattern-driven framework for stress detection, combining signal and image analysis. Image analysis of spectrograms and pattern techniques demonstrated superior accuracy in predicting stress compared to signal analysis alone.

Keywords:
Classification modelsFeature extractionLocal patterns techniquesSignal analysisSpectrograms

More Related Videos

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
08:25

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Published on: December 6, 2024

518
Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

10.9K

Related Experiment Videos

Last Updated: Sep 13, 2025

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
12:51

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

7.6K
Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
08:25

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Published on: December 6, 2024

518
Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli
09:16

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli

Published on: April 5, 2019

10.9K

Area of Science:

  • Physiology
  • Computer Science
  • Biomedical Engineering

Background:

  • Stress is a complex physiological and psychological response to challenges.
  • Accurate stress detection is crucial for mental and physical well-being.
  • Existing methods often rely on single data modalities, limiting comprehensive analysis.

Purpose of the Study:

  • To develop and evaluate a pattern-driven framework for stress detection using multi-modality sensor data.
  • To compare the effectiveness of signal-based and image-based analysis techniques for stress prediction.
  • To identify optimal feature extraction and classification methods for accurate stress identification.

Main Methods:

  • A pattern-driven framework integrating Local Binary Pattern (LBP), Local Normal Derivative Pattern (LNDP), Local Derivative Pattern (LDP), and Local Tetra Pattern (LTrP) using Spectrograms.
  • Extraction of features from Time Domain, Non-Linear chaos theory, Fractal Dimensions, and Histogram descriptors.
  • Binary classification using Support Vector Machine (SVM), Logistic Regression (LG), Ensemble methods, and AlexNet on multi-modality sensor data (HR, RR).

Main Results:

  • Image analysis, particularly using LBP, LNDP, and LTrP with Logistic Regression and Ensemble methods, achieved up to 100% classification accuracy.
  • LBP demonstrated strong performance across statistical (98.7%), entropy/histogram (100%), and fractal (90%) features.
  • Image analysis of spectrograms and pattern techniques yielded higher classification accuracy than signal analysis for stress prediction.

Conclusions:

  • The proposed pattern-driven framework, especially with image-based analysis of spectrograms, offers a highly accurate approach to stress detection.
  • Image analysis techniques, leveraging intricate patterns, outperform traditional signal analysis for stress prediction.
  • The study highlights the potential of advanced pattern recognition and machine learning for objective stress assessment.