Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases

Abstract

Purpose Lung diseases are the third leading cause of death worldwide. Stethoscope-based auscultation is the most commonly used, non-invasive, inexpensive, and primary diagnostic approach for assessing lung conditions. However, the manual auscultation-based diagnosis procedure is prone to error, and its accuracy is dependent on the physician’s experience and hearing capacity. Moreover, the stethoscope recording is vulnerable to different noises that can mask the important features of lung sounds which may lead to misdiagnosis. In this paper, a method for the acquisition of lung sound signals and classification of the top 7 lung diseases has been proposed for improving the efficacy of auscultation diagnosis of pulmonary disease. Methods An electronic stethoscope has been constructed for signal acquisition. Lung sound signals were then collected from people with COPD, upper respiratory tract infections (URTI), lower respiratory tract infections (LRTI), pneumonia, bronchiectasis, bronchiolitis, asthma, and healthy people. Lung sounds were analyzed using a wavelet multiresolution analysis. To choose the most relevant features, feature selection using one-way ANOVA was performed. The classification accuracy of various machine learning classifiers was compared, and the Fine Gaussian SVM was chosen for final classification due to its superior performance. Model optimization was accomplished through the application of Bayesian optimization techniques. Results A test classification accuracy of 99%, specificity of 99.2%, and sensitivity of 99.04%, have been achieved for the 7 lung diseases using the optimized Fine Gaussian SVM classifier. Conclusion Our experimental results demonstrate that the proposed method has the potential to be used as a decision support system for the classification of lung diseases, especially in those areas where the expertise and the means are limited.

Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

“…However, this technique has challenges such as the cooperation of patients in forced breathing, and requires a professional operator. Due to its high cost, this device can be used only in clinical settings, needs regular calibration, and is inefficient in detecting obstructive-restrictive abnormalities [2].…”

Real-world evaluation of the Stemoscope electronic tele-auscultation system

“…Tele-auscultation has become more critical in the past few years, especially with the emergence of COVID-19 [ 9 , 10 ]. There has been increased interest in the development of tele-auscultation systems to screen for heart and lung diseases [ 11 ]. We have previously used an electronic stethoscope (Stemoscope) (Hulu Devices) for tele-auscultation in the containment capsule at Leishenshan Hospital, Wuhan, China [ 5 ].…”

Using of Oximeter and Sound Analysis for Detection of Lung Lesions in Buffalo Calves

“…Viral and inflammatory diseases are due to introduction of this exogenous pyrogens into the body and inducing the hosts cell (macrophages) to produce and release endogenous pyrogens such as interleukin-1 and transmitted to the hypothalamic thermoregulatory centre where they induce synthesis of prostaglandins (PGE2) and raise the thermostatic set point to initiate the febrile response(7)(8) (9). The natural lung sounds are produced as a result of the movement of air through the tracheobronchial tree; thus, the sound becomes louder during inhalation compared to exhalation (10) (11). Auscultation of the lungs and airways is an important clinical examination of the respiratory system,.…”