Analysis of Respiratory Sounds: State of the Art

Author:

Reichert Sandra1,Gass Raymond1,Brandt Christian2,Andrès Emmanuel3

Affiliation:

1. Alcatel-Lucent, Chief Technical Office, Strasbourg, France.

2. Head of the Cardiology Department, Clinique Médicale B, CHRU Strasbourg, Strasbourg, France.

3. Head of the Internal Medicine Department, Clinique Médicale B, CHRU Strasbourg, Strasbourg, France.

Abstract

Objective This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds. Methods and material Review of the current medical and technological literature using Pubmed and personal experience. Results The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms… Conclusion The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools.

Publisher

SAGE Publications

Subject

Cardiology and Cardiovascular Medicine,Pulmonary and Respiratory Medicine

Cited by 69 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Detection of Wheeze Sounds in Respiratory Disorders: A Deep Learning Approach;International Advanced Researches and Engineering Journal;2024-04-20

2. Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction;Biomedical Engineering / Biomedizinische Technik;2024-03-21

3. Pediatric Pneumonia Diagnosis: Integration of a Self-Assembled Digital Stethoscope with Raspberry Pi and 1D CNN Model;2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS);2024-03-08

4. Advancing Auscultation Education: Signals Visualization as a Novel Tool for Enhancing Pathological Respiratory Sounds Detection;Polish Journal of Medical Physics and Engineering;2024-02-10

5. Pulmo-TS2ONN: A Novel Triple Scale Self Operational Neural Network for Pulmonary Disorder Detection Using Respiratory Sounds;IEEE Transactions on Instrumentation and Measurement;2024

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3