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

343
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...
343
Assessment of Airway, Skin Color, and Use of Accessory Muscles01:30

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

1.4K
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...
1.4K
Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

1.6K
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.
1.6K
Respiratory Volumes and Capacities I01:26

Respiratory Volumes and Capacities I

1.4K
Assessing the respiratory rate and rhythm for a complete minute is crucial for evaluating the breathing pattern. Even a minor increase in the patient's average respiratory rate, by as little as three to five breaths per minute, is an early and vital indicator of respiratory distress. Patients with a respiratory rate exceeding twenty-four breaths per minute require close monitoring to determine the physiological alterations. This careful observation is essential for prompt recognition and...
1.4K
Physical Assessment of the Respiratory Tract II: Inspection01:27

Physical Assessment of the Respiratory Tract II: Inspection

692
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...
692
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

2.1K
Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
2.1K

You might also read

Related Articles

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

Sort by
Same author

Effectiveness, pharmacokinetics, and safety of triptorelin acetate microspheres in patients with locally advanced and metastatic prostate cancer.

Therapeutic advances in medical oncology·2024
Same author

Effects of vegetation restoration on soil aggregate characteristics and soil erodibility at gully head in Loess hilly and gully region.

Scientific reports·2024
Same author

Predicting Preoperative Risk and Prognosis in Patients with Proliferative Hepatocellular Carcinoma.

Academic radiology·2024
Same author

Similar pipeline experiment and disaster control emergency plan of updraft airflow fire in mine.

Scientific reports·2024
Same author

Effect of AM fungi on the growth and powdery mildew development of Astragalus sinicus L. under water stress.

Plant physiology and biochemistry : PPB·2024
Same author

Fumarate Hydratase-Deficient Renal Cell Carcinoma With Paraganglioma Detected on 18 F-FDG PET/CT.

Clinical nuclear medicine·2024

Related Experiment Video

Updated: Dec 3, 2025

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
07:54

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

20.3K

Severity evaluation of obstructive sleep apnea based on speech features.

Yiming Ding1,2,3, Jiaxi Wang4, Jiandong Gao4,5

  • 1Beijing Tongren Hospital, Capital Medical University, 1, Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, People's Republic of China.

Sleep & Breathing = Schlaf & Atmung
|October 28, 2020
PubMed
Summary

Machine learning accurately predicts obstructive sleep apnea (OSA) severity using Chinese speech signals. This method offers an effective way to screen for OSA, utilizing pronunciation as a key indicator.

Keywords:
Machine learningObstructive sleep apnea (OSA)Speech signal processing

More Related Videos

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS
12:43

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

Published on: February 21, 2011

35.7K
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

276

Related Experiment Videos

Last Updated: Dec 3, 2025

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
07:54

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

20.3K
A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS
12:43

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

Published on: February 21, 2011

35.7K
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

276

Area of Science:

  • Speech Signal Processing
  • Machine Learning
  • Sleep Medicine

Background:

  • Obstructive sleep apnea (OSA) is linked to upper airway abnormalities.
  • Speech signal characteristics differ between individuals with and without OSA.

Purpose of the Study:

  • To evaluate obstructive sleep apnea (OSA) severity automatically using machine learning and speech signals from Chinese individuals.
  • To assess the effectiveness of speech analysis in predicting the apnea-hypopnea index (AHI).

Main Methods:

  • 151 adult male Mandarin speakers with suspected OSA underwent polysomnography.
  • Speech signals (vowels, nasal sounds) were recorded in sitting and supine positions.
  • Machine learning, specifically linear support vector machine (SVM) with linear prediction cepstral coefficients (LPCC), was used to analyze speech features.

Main Results:

  • The machine learning model achieved 78.8% accuracy in classifying OSA severity at AHI thresholds of 30 and 10 events/h.
  • Sensitivities were 77.3% and 79.1%, and specificities were 80.3% and 78.0% for the respective thresholds.
  • Features extracted from Chinese pronunciation proved effective in predicting OSA.

Conclusions:

  • A severity evaluation model for OSA was developed using speech signal processing and machine learning.
  • This approach serves as an effective screening tool for patients with OSA.
  • Chinese pronunciation features are valuable predictors for OSA.