Approaches for Respiratory Sound Analysis in Identification of Respiratory Diseases

Arunkumar  Ram; Ghanshyam  Jindal; Uttam  Bagal; Gajanan  Nagare

doi:10.18502/fbt.v11i2.15347

Arunkumar Ram Department of Biomedical Engineering, MGM’s College of Engineering & Technology, Navi Mumbai, India
Ghanshyam Jindal Department of Biomedical Engineering, MGM’s College of Engineering & Technology, Navi Mumbai, India
Uttam Bagal Department of Biomedical Engineering, MGM’s College of Engineering & Technology, Navi Mumbai, India
Gajanan Nagare Department of Biomedical Engineering, Vidyalankar Institute of Technology, Mumbai, India

DOI: https://doi.org/10.18502/fbt.v11i2.15347

Keywords: Respiratory Sound Analysis; Respiratory Sound Classification; Adventitious Respiratory Sounds; Datasets; Spectral Analysis; Time Frequency Analysis.

Abstract

Purpose: Medical professionals throughout the world prefer to use conventional stethoscopes to listen to respiratory sounds. Listening to respiratory sounds through stethoscopes is a subjective matter, and proper diagnosis of the disease depends on the skills and ability of the doctor. Computerized analysis of respiratory sounds can help doctors and researchers to characterize different abnormal respiratory patterns and make informed decisions.

Materials and Methods: This study includes previously reported work in different normal and abnormal respiratory sounds. The IEEE, PubMed, Google Scholar and Elsevier databases were searched and studies with the keywords of lung sound analysis, respiratory sound analysis, and respiratory sound classification were included. Detailed characteristics of normal and abnormal respiratory sounds are mentioned. In addition, Time-amplitude characteristics of different respiratory sound plots are obtained using MATLAB and ICBHI database. This study systematically discusses different approaches for respiratory sound analysis like visual analysis of the time-amplitude signals, frequency analysis, and spectral analysis using fast Fourier transform, statistical analysis, and machine learning approach. A list of relevant datasets is mentioned that can help researchers to do further analysis in this domain.

Results: The careful observations and analysis show the possibility of predicting respiratory diseases by extracting suitable parameters such as the frequency response and spectral characteristics of the signal. Power spectral density can help us to calculate the maximum, median frequency over an extended period. Using machine learning we can estimate the energy, entropy, spectral features, and wavelets of the signals.

Conclusion: Computer-based respiratory sound analysis can help medical professionals in making informed decisions. This will help in early diagnosis and devise effective treatment plans for the patients.