Author ORCID Identifier
Document Type
Thesis
Date of Award
12-2021
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Dr. KC Santosh
Abstract
Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds.
Subject Categories
Computer and Systems Architecture
Keywords
Respiratory, Sound Analysis, Lung Health
Number of Pages
66
Publisher
University of South Dakota
Recommended Citation
Sreerama, Priyanka, "Respiratory Sound Analysis for the Evidence of Lung Health" (2021). Dissertations and Theses. 5.
https://red.library.usd.edu/diss-thesis/5