Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Assessment of Ventilation II: Respiratory Depth and Rhythm

Assessment of Ventilation II: Respiratory Depth and Rhythm

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:

You might also read

Related Articles

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

Sort by

Same author

Acute Deterioration in a Ventilated Preterm Infant.

Journal of paediatrics and child health·2026

Same author

Progress in cell therapies for neonatal conditions: Proceedings of the Third Neonatal Cell Therapies Symposium (2025).

Pediatric research·2026

Same author

Hippocampal growth and function are reduced in the newborn following fetal growth restriction.

Brain : a journal of neurology·2026

Same author

Beyond survival: growth trajectories in necrotizing enterocolitis.

Pediatric research·2026

Same author

In vivo and ex vivo cardiovascular haemodynamic responses of preterm growth-restricted lambs to perinatal asphyxia.

Clinical science (London, England : 1979)·2025

Same author

An intelligent, compact wearable pressure-strain combo sensor system for continuous fetal movement monitoring.

Science advances·2025

Search research articles

Related Experiment Video

Updated: Jun 14, 2026

International Expert Consensus and Recommendations for Neonatal Pneumothorax Ultrasound Diagnosis and Ultrasound-guided Thoracentesis Procedure

International Expert Consensus and Recommendations for Neonatal Pneumothorax Ultrasound Diagnosis and Ultrasound-guided Thoracentesis Procedure

Published on: March 12, 2020

NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning.

Yang Yi Poh¹, Ethan Grooby^1,2, Kenneth Tan³

¹Department of Electrical and Computer Systems EngineeringMonash University, Melbourne Clayton VIC 3800 Australia.

IEEE Open Journal of Engineering in Medicine and Biology

|June 20, 2024

Summary

This summary is machine-generated.

A new deep learning model, NeoSSNet, effectively separates neonatal heart and lung sounds, improving diagnostic accuracy. This faster method enhances health monitoring systems by isolating specific chest sounds.

Keywords:

Deep learning heart sound lung sound phonocardiogram (PCG)single-channel sound separation

More Related Videos

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

Published on: April 7, 2023

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Related Experiment Videos

Last Updated: Jun 14, 2026

International Expert Consensus and Recommendations for Neonatal Pneumothorax Ultrasound Diagnosis and Ultrasound-guided Thoracentesis Procedure

International Expert Consensus and Recommendations for Neonatal Pneumothorax Ultrasound Diagnosis and Ultrasound-guided Thoracentesis Procedure

Published on: March 12, 2020

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn

Published on: April 7, 2023

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Biomedical Engineering
Artificial Intelligence in Medicine
Neonatal Health

Background:

Auscultation is crucial for diagnosing neonatal cardiovascular and respiratory conditions.
Acquiring clear, isolated heart or lung sounds from neonatal chest recordings is challenging.

Purpose of the Study:

To introduce NeoSSNet, a novel deep learning model for neonatal chest sound separation.
To evaluate NeoSSNet's performance against existing methods for improved diagnostic accuracy.

Main Methods:

A masked-based deep learning architecture (NeoSSNet) utilizing 1D convolutions and a transformer for mask generation.
Encoding chest sounds into tokens, applying generated heart and lung sound masks, and decoding back to waveforms.

Main Results:

NeoSSNet demonstrated superior performance over previous methods, with objective distortion improvements ranging from 2.01 dB to 5.06 dB.
The model achieved a significant speed improvement, being at least 17 times faster than existing techniques.

Conclusions:

NeoSSNet offers a promising solution for accurate neonatal chest sound separation.
The model can serve as an effective preprocessing step for health monitoring systems requiring isolated heart or lung sounds.