A novel method for automatic identification of respiratory disease from acoustic recordings.

This paper evaluates the use of breath sound recordings to automatically determine the respiratory health status of a subject. A number of features were investigated and Wilcoxon Rank Sum statistical test was used to determine the significance of the extracted features. The significant features were then passed to a feature selection algorithm based on mutual information, to determine the combination of features that provided minimal redundancy and maximum relevance. The algorithm was tested on a publicly accessible respiratory sounds database. With the testing dataset, the trained classifier achieved accuracy of 87.1%, sensitivity of 86.8% and specificity of 93.6%. These are promising results showing the possibility of determining the presence or absence of respiratory disease using breath sounds recordings

Respiratory Sound Analysis for the Evidence of Lung Health

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