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

Sleep Apnea01:21

Sleep Apnea

857
Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
857
Classification of Signals01:30

Classification of Signals

1.7K
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...
1.7K
Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

3.7K
Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
3.7K
Assessment of Airway, Skin Color, and Use of Accessory Muscles01:30

Assessment of Airway, Skin Color, and Use of Accessory Muscles

2.0K
A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...
2.0K
Heart Sounds01:15

Heart Sounds

4.5K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
4.5K
Physical Assessment of the Respiratory Tract II: Inspection01:27

Physical Assessment of the Respiratory Tract II: Inspection

1.4K
Physical assessment of the respiratory tract through inspection is a crucial step in understanding the patient's respiratory health. It provides insights into the functioning of the respiratory system, the musculoskeletal structure, and even the patient's nutritional status. This comprehensive approach involves observing several vital aspects: chest configuration, breathing patterns, respiratory rates, skin color, and use of accessory muscles.
Chest Configuration
The chest configuration...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Evaluation and Comparison of the UV-LED Action Spectra for Photochemical Disinfection of Coliphages and Human Pathogenic Viruses.

Microorganisms·2025
Same author

Relationship between coffee concentration and bowel motility using bowel-sound-based stimulus-response plots in healthy individuals.

Scientific reports·2025
Same author

Viral Inactivation by Light-Emitting Diodes: Action Spectra Reveal Genomic Damage as the Primary Mechanism.

Viruses·2025
Same author

Sensor arrangement strategy for effective bowel sound source localization.

Biomedical engineering letters·2025
Same author

Efficacy of ultraviolet-light emitting diodes in bacterial inactivation and DNA damage via sensitivity evaluation using multiple wavelengths and bacterial strains.

Archives of microbiology·2025
Same author

Reduction of Campylobacter jejuni contamination by using UVA-LED and sodium hypochlorite on the surface of chicken meat.

Journal of microorganism control·2024

Related Experiment Video

Updated: Apr 20, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.3K

Signal shape feature for automatic snore and breathing sounds classification.

Takahiro Emoto1, Masato Kashihara, Udantha R Abeyratne

  • 1Institute of Technology and Science, the University of Tokushima, Japan.

Physiological Measurement
|November 18, 2014
PubMed
Summary

A novel automated method accurately classifies snore and breathing sounds using the positive/negative amplitude ratio (PNAR) feature. This technique shows high sensitivity and specificity, advancing sleep study sound analysis.

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

721
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

1.1K

Related Experiment Videos

Last Updated: Apr 20, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.3K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

721
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

1.1K

Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Acoustic Signal Processing

Background:

  • Accurate snore and breathing sound classification is crucial for sleep studies.
  • Existing snore analysis techniques often lack reliable automatic sound classification methods.

Purpose of the Study:

  • To propose an automated method for classifying snore and breathing sounds.
  • To introduce and evaluate a novel feature, the positive/negative amplitude ratio (PNAR), for sound signal shape measurement.

Main Methods:

  • Development of an automated sound classification method utilizing the PNAR feature.
  • Evaluation of the method using a dataset of 22,643 snore and 4664 breathing sound episodes from 40 subjects.
  • Performance assessment using Receiver Operating Characteristic (ROC) analysis.

Main Results:

  • The proposed PNAR-based method achieved 0.923 sensitivity and 0.918 specificity in classifying snore and breathing sounds.
  • The PNAR feature effectively measures sound signal shape for improved classification.
  • Demonstrated high accuracy in distinguishing between snore and breathing sounds on test data.

Conclusions:

  • The positive/negative amplitude ratio (PNAR) is a promising feature for automated snore and breathing sound classification.
  • This method has significant potential for integration into non-contact sleep study technologies.
  • The findings support the advancement of acoustic analysis in sleep medicine.