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Srivastava A, Jain S, Miranda R, et al. Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ Comput Sci. 2021;7:e369doi: 10.7717/peerj-cs.369.
Srivastava, A., Jain, S., Miranda, R., Patil, S., Pandya, S., & Kotecha, K. (2021). Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ. Computer science, 7e369. https://doi.org/10.7717/peerj-cs.369
Srivastava, Arpan, et al. "Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease." PeerJ. Computer science vol. 7 (2021): e369. doi: https://doi.org/10.7717/peerj-cs.369
Srivastava A, Jain S, Miranda R, Patil S, Pandya S, Kotecha K. Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. PeerJ Comput Sci. 2021 Feb 11;7:e369. doi: 10.7717/peerj-cs.369. eCollection 2021. PMID: 33817019; PMCID: PMC7959628.
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PeerJ Comput Sci. 2021 Feb 11;7:e369. doi: 10.7717/peerj-cs.369. eCollection 2021.

Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease.

PeerJ. Computer science

Arpan Srivastava, Sonakshi Jain, Ryan Miranda, Shruti Patil, Sharnil Pandya, Ketan Kotecha

Affiliations

  1. CS&IT Dept, Symbiosis Insitute of Technology, Symbiosis International (Deemed University), Pune, Maharastra, India.
  2. Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharastra, India.

PMID: 33817019 PMCID: PMC7959628 DOI: 10.7717/peerj-cs.369

Abstract

In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain's challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.

© 2021 Srivastava et al.

Keywords: CNN based classification; Deep learning; Machine learning; Medical-assistive technology; Respiratory sound analysis

Conflict of interest statement

The authors declare that they have no competing interests.

References

  1. Med Image Anal. 2016 Oct;33:44-49 - PubMed
  2. Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:804-807 - PubMed
  3. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:164-167 - PubMed
  4. IEEE J Biomed Health Inform. 2014 May;18(3):731-8 - PubMed
  5. J Med Syst. 2008 Oct;32(5):429-32 - PubMed
  6. Sci Rep. 2018 Aug 3;8(1):11645 - PubMed
  7. IEEE J Biomed Health Inform. 2018 Jan;22(1):285-290 - PubMed
  8. Comput Methods Programs Biomed. 2012 Mar;105(3):183-93 - PubMed
  9. IEEE J Biomed Health Inform. 2019 Jul 26;: - PubMed
  10. Physiol Meas. 2019 Mar 22;40(3):035001 - PubMed

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