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Convolutional Neural Network for Breathing Phase Detection in Lung Sounds

https://doi.org/10.3390/s19081798

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s10439-016-1554-1openalex-snapshot

Towards the Development of a Mobile Phonopneumogram: Automatic Breath-Phase Classification Using Smartphones

inferredcites →https://doi.org/10.1007/s11263-013-0620-5openalex-snapshot

Selective Search for Object Recognition

inferredcites →https://doi.org/10.1007/s11263-014-0733-5openalex-snapshot

The Pascal Visual Object Classes Challenge: A Retrospective

inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot

ImageNet Large Scale Visual Recognition Challenge

inferredcites →https://doi.org/10.1007/s11517-012-0869-9openalex-snapshot

Acoustic breath-phase detection using tracheal breath sounds

inferredcites →https://doi.org/10.1016/j.procs.2015.08.592openalex-snapshot

Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering

inferredcites →https://doi.org/10.1016/j.resp.2008.02.002openalex-snapshot

Impact of breathing pattern on work of breathing in healthy subjects and patients with COPD

inferredcites →https://doi.org/10.1056/nejmra1302901openalex-snapshot

Fundamentals of Lung Auscultation

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/icassp.2018.8461870openalex-snapshot

The Microsoft 2017 Conversational Speech Recognition System

inferredcites →https://doi.org/10.1109/tpami.2012.28openalex-snapshot

Measuring the Objectness of Image Windows

inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

inferredcites →https://doi.org/10.1111/resp.13173openalex-snapshot

Novel assessment tool to detect breathing pattern disorder in patients with refractory asthma

inferredcites →https://doi.org/10.1136/bmjresp-2016-000136openalex-snapshot

Wheezes, crackles and rhonchi: simplifying description of lung sounds increases the agreement on their classification: a study of 12 physicians' classification of lung sounds from video recordings

inferredcites →https://doi.org/10.1136/bmjresp-2017-000250openalex-snapshot

International perception of lung sounds: a comparison of classification across some European borders

inferredcites →https://doi.org/10.1183/09031936.95.08122139openalex-snapshot

Crackles: recording, analysis and clinical significance

inferredcites →https://doi.org/10.1214/aos/1176344552openalex-snapshot

Bootstrap Methods: Another Look at the Jackknife

inferredcites →https://doi.org/10.21437/interspeech.2017-405openalex-snapshot

English Conversational Telephone Speech Recognition by Humans and Machines

inferredcites →https://doi.org/10.2307/2529310openalex-snapshot

The Measurement of Observer Agreement for Categorical Data

inferredcites →https://doi.org/10.4187/respcare.04987openalex-snapshot

Computerized Respiratory Sounds: Novel Outcomes for Pulmonary Rehabilitation in COPD

inferredcites →https://doi.org/10.5220/0003756301830188openalex-snapshot

RESPIRATORY SOUND ANNOTATION SOFTWARE

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An Algorithm for Automatic Breathing Phase Detection in Recorded Lung Sounds

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Solving Image Processing Critical Problems Using Machine Learning

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Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds

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Leveraging CQT-VMD and pre-trained AlexNet architecture for accurate pulmonary disease classification from lung sound signals

inferred← citeshttps://doi.org/10.1007/s10916-025-02315-8openalex-snapshot

Lightweight Hybrid Deep Learning Models for Accurate Classification of Respiratory Conditions from Raw Lung Sounds

inferred← citeshttps://doi.org/10.1007/s11831-025-10344-2openalex-snapshot

Artificial Intelligence Based Techniques to Detect and Classify Adventitious Respiratory Sounds: An in-Depth Review

inferred← citeshttps://doi.org/10.1007/s42979-025-04142-7openalex-snapshot

Breathe-Run-Guide: A Review of Personal Breathing Guidance Systems in Running

inferred← citeshttps://doi.org/10.1016/b978-0-12-823447-1.00001-4openalex-snapshot

Respiratory image analysis

inferred← citeshttps://doi.org/10.1016/b978-0-12-823447-1.00003-8openalex-snapshot

Automated respiratory sound analysis

inferred← citeshttps://doi.org/10.1016/j.apacoust.2024.110390openalex-snapshot

Estimation of lung sound cycle span using spectro-temporal respiratory frequency evaluation

inferred← citeshttps://doi.org/10.1016/j.asoc.2021.107465openalex-snapshot

Click-event sound detection in automotive industry using machine/deep learning

inferred← citeshttps://doi.org/10.1016/j.bspc.2021.102947openalex-snapshot

Gammatonegram based triple classification of lung sounds using deep convolutional neural network with transfer learning

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105222openalex-snapshot

A dual-purpose deep learning model for auscultated lung and tracheal sound analysis based on mixed set training

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105451openalex-snapshot

Ensemble deep learning model for dimensionless respiratory airflow estimation using respiratory sound

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106315openalex-snapshot

Automatic breathing phase identification based on the second derivative of the recorded lung sounds

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108491openalex-snapshot

EZhouNet: A framework based on graph neural network and anchor interval for the respiratory sound event detection

inferred← citeshttps://doi.org/10.1016/j.chpulm.2024.100079openalex-snapshot

Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?

inferred← citeshttps://doi.org/10.1016/j.cmpb.2023.107720openalex-snapshot

BRACETS: Bimodal repository of auscultation coupled with electrical impedance thoracic signals

inferred← citeshttps://doi.org/10.1016/j.smhl.2022.100329openalex-snapshot

Enabling effective breathing sound analysis for automated diagnosis of lung diseases

inferred← citeshttps://doi.org/10.1038/s41598-025-98546-3openalex-snapshot

Real-time airway monitoring system using binary classification model based on respiratory sounds of rabbits with a tracheostomy tube

inferred← citeshttps://doi.org/10.1063/5.0179187openalex-snapshot

Diagnosis of pneumonia from lung sounds using deep learning

inferred← citeshttps://doi.org/10.1080/17476348.2025.2511223openalex-snapshot

Lung auscultation – today and tomorrow- a narrative review

inferred← citeshttps://doi.org/10.1088/1361-6579/ada9c0openalex-snapshot

Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter

inferred← citeshttps://doi.org/10.1088/1361-6579/adaf08openalex-snapshot

REDT: a specialized transformer model for the respiratory phase and adventitious sound detection

inferred← citeshttps://doi.org/10.1109/access.2020.3004349openalex-snapshot

Adaptive Blowing Interaction Method Based on a Siamese Network

inferred← citeshttps://doi.org/10.1109/access.2023.3313605openalex-snapshot

Intelligent Detection of Adventitious Sounds Critical in Diagnosing Cardiovascular and Cardiopulmonary Diseases

inferred← citeshttps://doi.org/10.1109/access.2026.3657941openalex-snapshot

Breathing Cycle Detection System for Respiratory Sounds Using Deep Learning

inferred← citeshttps://doi.org/10.1109/ai-csp52968.2021.9671124openalex-snapshot

VGG16, ResNet-50, and GoogLeNet Deep Learning Architecture for Breathing Sound Classification: A Comparative Study

inferred← citeshttps://doi.org/10.1109/ai-csp52968.2021.9671211openalex-snapshot

Three ResNet Deep Learning Architectures Applied in Pulmonary Pathologies Classification

inferred← citeshttps://doi.org/10.1109/biocas58349.2023.10388733openalex-snapshot

Pediatric Respiratory Sound Classification Using a Dual Input Deep Learning Architecture

inferred← citeshttps://doi.org/10.1109/bsn56160.2022.9928520openalex-snapshot

Real-Time Breathing Phase Detection Using Earbuds Microphone

inferred← citeshttps://doi.org/10.1109/embc44109.2020.9176226openalex-snapshot

Breathing Sound Segmentation and Detection Using Transfer Learning Techniques on an Attention-Based Encoder-Decoder Architecture

inferred← citeshttps://doi.org/10.1109/embc46164.2021.9630577openalex-snapshot

Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1109/embc46164.2021.9630734openalex-snapshot

Detection of squawks in respiratory sounds of mechanically ventilated COVID-19 patients

inferred← citeshttps://doi.org/10.1109/embc48229.2022.9871897openalex-snapshot

Estimation of Respiratory Rate from Breathing Audio

inferred← citeshttps://doi.org/10.1109/icaccm61117.2024.11058992openalex-snapshot

AI-Driven Multimodal Approaches to Global Disease Management: A Review

inferred← citeshttps://doi.org/10.1109/iccasit66611.2025.11348789openalex-snapshot

Research on Respiratory Signal Recognition Based on Convolutional Neural Network

inferred← citeshttps://doi.org/10.1109/iccca52192.2021.9666346openalex-snapshot

Respiratory Disease Classification by CNN using MFCC

inferred← citeshttps://doi.org/10.1109/icfsp62546.2024.10785273openalex-snapshot

Diagnosis Through Adventitious Sounds Detection and Respiratory Phases Identification Using XGBoost

inferred← citeshttps://doi.org/10.1109/iciip53038.2021.9702593openalex-snapshot

Diagnose and Prognose the Syndrome of Respiratory Disorder Through Breathing Pattern Deploying DenseNet-SVM Model

inferred← citeshttps://doi.org/10.1109/icit58056.2023.10225790openalex-snapshot

VGG16-Based Deep Learning Architectures for Classification of Lung Sounds into Normal, Crackles, and Wheezes using Gammatonegrams

inferred← citeshttps://doi.org/10.1109/icmla66185.2025.00166openalex-snapshot

Detecting Patient and Healthy People’s Personalized Breathing Patterns with Few-Shot Learning

inferred← citeshttps://doi.org/10.1109/ismar55827.2022.00036openalex-snapshot

Towards Spatial Airflow Interaction: Schlieren Imaging for Augmented Reality

inferred← citeshttps://doi.org/10.1109/jbhi.2021.3123353openalex-snapshot

Comprehensive Analysis System for Automated Respiratory Cycle Segmentation and Crackle Peak Detection

inferred← citeshttps://doi.org/10.1109/jbhi.2022.3144314openalex-snapshot

Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings

inferred← citeshttps://doi.org/10.1109/jbhi.2023.3319381openalex-snapshot

Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study

inferred← citeshttps://doi.org/10.1109/ssd54932.2022.9955783openalex-snapshot

Gammatonegram based Pulmonary Pathologies Classification using Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1117/12.2590770openalex-snapshot

Development of a respiratory sound labeling software for training a deep learning-based respiratory sound analysis model

inferred← citeshttps://doi.org/10.1371/journal.pone.0254134openalex-snapshot

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

inferred← citeshttps://doi.org/10.1515/bmt-2022-0180openalex-snapshot

A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs

inferred← citeshttps://doi.org/10.23919/icact60172.2024.10471976openalex-snapshot

Time-frequency Analysis for Validating Prognostics Algorithms of Rolling Element Bearings

inferred← citeshttps://doi.org/10.24425/aoa.2021.136583openalex-snapshot

Transmission Perspective on the Mechanism of Coarse and Fine Crackle Sounds

inferred← citeshttps://doi.org/10.29109/gujsc.758325openalex-snapshot

Akciğer Seslerinin Derin Öğrenme İle Sınıflandırılması

inferred← citeshttps://doi.org/10.3389/fmed.2023.1269784openalex-snapshot

Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

inferred← citeshttps://doi.org/10.3390/app12157623openalex-snapshot

A Progressively Expanded Database for Automated Lung Sound Analysis: An Update

inferred← citeshttps://doi.org/10.3390/s20041214openalex-snapshot

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

inferred← citeshttps://doi.org/10.35940/ijeat.a3207.1011121openalex-snapshot

Classification of Lung Sounds and Disease Prediction using Dense CNN Network

inferred← citeshttps://doi.org/10.48550/arxiv.2008.02900openalex-snapshot

Respiratory Sound Classification Using Long-Short Term Memory

inferred← citeshttps://doi.org/10.48550/arxiv.2102.04062openalex-snapshot

An Update on a Progressively Expanded Database for Automated Lung Sound Analysis

inferred← citeshttps://doi.org/10.48550/arxiv.2104.14921openalex-snapshot

Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolitional Neural Networks

inferred← citeshttps://doi.org/10.70675/960d753dz04e2z4682z943fz88d5a7eae334openalex-snapshot

Analyse et diagnostic des troubles respiratoires du sommeil à l’aide des sons trachéaux

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