Literature DB >> 24114889

Artificial intelligence techniques used in respiratory sound analysis--a systematic review.

Rajkumar Palaniappan, Kenneth Sundaraj, Sebastian Sundaraj.   

Abstract

Artificial intelligence (AI) has recently been established as an alternative method to many conventional methods. The implementation of AI techniques for respiratory sound analysis can assist medical professionals in the diagnosis of lung pathologies. This article highlights the importance of AI techniques in the implementation of computer-based respiratory sound analysis. Articles on computer-based respiratory sound analysis using AI techniques were identified by searches conducted on various electronic resources, such as the IEEE, Springer, Elsevier, PubMed, and ACM digital library databases. Brief descriptions of the types of respiratory sounds and their respective characteristics are provided. We then analyzed each of the previous studies to determine the specific respiratory sounds/pathology analyzed, the number of subjects, the signal processing method used, the AI techniques used, and the performance of the AI technique used in the analysis of respiratory sounds. A detailed description of each of these studies is provided. In conclusion, this article provides recommendations for further advancements in respiratory sound analysis.

Mesh:

Year:  2014        PMID: 24114889     DOI: 10.1515/bmt-2013-0074

Source DB:  PubMed          Journal:  Biomed Tech (Berl)        ISSN: 0013-5585            Impact factor:   1.411


  6 in total

1.  Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.

Authors:  Alban Glangetas; Mary-Anne Hartley; Aymeric Cantais; Delphine S Courvoisier; David Rivollet; Deeksha M Shama; Alexandre Perez; Hervé Spechbach; Véronique Trombert; Stéphane Bourquin; Martin Jaggi; Constance Barazzone-Argiroffo; Alain Gervaix; Johan N Siebert
Journal:  BMC Pulm Med       Date:  2021-03-24       Impact factor: 3.317

Review 2.  The coming era of a new auscultation system for analyzing respiratory sounds.

Authors:  Yoonjoo Kim; YunKyong Hyon; Sunju Lee; Seong-Dae Woo; Taeyoung Ha; Chaeuk Chung
Journal:  BMC Pulm Med       Date:  2022-03-31       Impact factor: 3.317

3.  Digital auscultation as a diagnostic aid to detect childhood pneumonia: A systematic review.

Authors:  Salahuddin Ahmed; Saima Sultana; Ahad M Khan; Mohammad S Islam; Gm Monsur Habib; Ian M McLane; Eric D McCollum; Abdullah H Baqui; Steven Cunningham; Harish Nair
Journal:  J Glob Health       Date:  2022-04-23       Impact factor: 4.413

4.  A temporal dependency feature in lower dimension for lung sound signal classification.

Authors:  Amy M Kwon; Kyungtae Kang
Journal:  Sci Rep       Date:  2022-05-12       Impact factor: 4.996

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

Authors:  Ali Mohammad Alqudah; Shoroq Qazan; Yusra M Obeidat
Journal:  Soft comput       Date:  2022-09-26       Impact factor: 3.732

6.  A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

Authors:  Rajkumar Palaniappan; Kenneth Sundaraj; Sebastian Sundaraj
Journal:  BMC Bioinformatics       Date:  2014-06-27       Impact factor: 3.169

  6 in total

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