scholarly journals VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds

2022 ◽  
pp. 105220
Author(s):  
Fabrizio Pancaldi ◽  
Giuseppe Stefano Pezzuto ◽  
Giulia Cassone ◽  
Marianna Morelli ◽  
Andreina Manfredi ◽  
...  
Keyword(s):  
Lung Sounds

scholarly journals A dataset of lung sounds recorded from the chest wall using an electronic stethoscope

Data in Brief ◽  
2021 ◽  
pp. 106913
Author(s):  
Mohammad Fraiwan ◽  
Luay Fraiwan ◽  
Basheer Khassawneh ◽  
Ali Ibnian
Keyword(s):  
Chest Wall ◽  
Lung Sounds ◽  
Electronic Stethoscope

ALSD-Net: Automatic lung sounds diagnosis network from pulmonary signals

2021 ◽  
Author(s):  
Neeraj Baghel ◽  
Vivek Nangia ◽  
Malay Kishore Dutta
Keyword(s):  
Lung Sounds

A telemedicine tool framework for lung sounds classification using ensemble classifier algorithms

Measurement ◽  
2020 ◽  
Vol 162 ◽  
pp. 107883
Author(s):  
Mustafa Musa Jaber ◽  
Sura Khalil Abd ◽  
P.Mohamed Shakeel ◽  
M.A. Burhanuddin ◽  
Mohammed Abdulameer Mohammed ◽  
...  
Keyword(s):  
Ensemble Classifier ◽  
Lung Sounds ◽  
Classifier Algorithms

scholarly journals Crackle Pitch Rises Progressively during Inspiration in Pneumonia, CHF, and IPF Patients

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Andrey Vyshedskiy ◽  
Raymond Murphy
Keyword(s):  
Heart Failure ◽  
Lung Volume ◽  
Interstitial Fibrosis ◽  
Lung Sounds ◽  
Lung Sound ◽  
Lung Volumes ◽  
Alternate Explanation ◽  
40 Hz

Objective. It is generally accepted that crackles are due to sudden opening of airways and that larger airways produce crackles of lower pitch than smaller airways do. As larger airways are likely to open earlier in inspiration than smaller airways and the reverse is likely to be true in expiration, we studied crackle pitch as a function of crackle timing in inspiration and expiration. Our goal was to see if the measurement of crackle pitch was consistent with this theory.Methods. Patients with a significant number of crackles were examined using a multichannel lung sound analyzer. These patients included 34 with pneumonia, 38 with heart failure, and 28 with interstitial fibrosis.Results. Crackle pitch progressively increased during inspirations in 79% of all patients. In these patients crackle pitch increased by approximately 40 Hz from the early to midinspiration and by another 40 Hz from mid to late-inspiration. In 10% of patients, crackle pitch did not change and in 11% of patients crackle pitch decreased. During expiration crackle pitch progressively decreased in 72% of patients and did not change in 28% of patients.Conclusion. In the majority of patients, we observed progressive crackle pitch increase during inspiration and decrease during expiration. Increased crackle pitch at larger lung volumes is likely a result of recruitment of smaller diameter airways. An alternate explanation is that crackle pitch may be influenced by airway tension that increases at greater lung volume. In any case improved understanding of the mechanism of production of these common lung sounds may help improve our understanding of pathophysiology of these disorders.

scholarly journals Detection of pathological mechano-acoustic signatures using precision accelerometer contact microphones in patients with pulmonary disorders

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pranav Gupta ◽  
Haoran Wen ◽  
Lorenzo Di Francesco ◽  
Farrokh Ayazi
Keyword(s):  
Pulmonary Disease ◽  
Acute Decompensated Heart Failure ◽  
Healthcare Delivery ◽  
Acoustic Signals ◽  
Chronic Obstructive ◽  
Lung Sounds ◽  
Breathing Patterns ◽  
Clinical Biomarkers ◽  
Breath Sounds ◽  
Sensor Module

AbstractMonitoring pathological mechano-acoustic signals emanating from the lungs is critical for timely and cost-effective healthcare delivery. Adventitious lung sounds including crackles, wheezes, rhonchi, bronchial breath sounds, stridor or pleural rub and abnormal breathing patterns function as essential clinical biomarkers for the early identification, accurate diagnosis and monitoring of pulmonary disorders. Here, we present a wearable sensor module comprising of a hermetically encapsulated, high precision accelerometer contact microphone (ACM) which enables both episodic and longitudinal assessment of lung sounds, breathing patterns and respiratory rates using a single integrated sensor. This enhanced ACM sensor leverages a nano-gap transduction mechanism to achieve high sensitivity to weak high frequency vibrations occurring on the surface of the skin due to underlying lung pathologies. The performance of the ACM sensor was compared to recordings from a state-of-art digital stethoscope, and the efficacy of the developed system is demonstrated by conducting an exploratory research study aimed at recording pathological mechano-acoustic signals from hospitalized patients with a chronic obstructive pulmonary disease (COPD) exacerbation, pneumonia, and acute decompensated heart failure. This unobtrusive wearable system can enable both episodic and longitudinal evaluation of lung sounds that allow for the early detection and/or ongoing monitoring of pulmonary disease.

Lung sounds in neonates with and without an added dead space

1995 ◽  
Vol 19 (6) ◽  
pp. 348-354 ◽  
Author(s):  
R. W. Blowes ◽  
P. Yiallouros ◽  
A. D. Milner
Keyword(s):  
Dead Space ◽  
Lung Sounds

Automated classification of human lung sound signals using phase space representation of intrinsic mode function

2021 ◽  
Author(s):  
Sibghatullah I. Khan ◽  
Vikram Palodiya ◽  
Lavanya Poluboyina
Keyword(s):  
Phase Space ◽  
Human Lung ◽  
Feature Space ◽  
Normal Lung ◽  
Space Representation ◽  
Support Vector ◽  
Lung Sounds ◽  
Lung Sound ◽  
Phase Space Representation

Abstract Bronchiectasis and chronic obstructive pulmonary disease (COPD) are common human lung diseases. In general, the expert pulmonologistcarries preliminary screening and detection of these lung abnormalities by listening to the adventitious lung sounds. The present paper is an attempt towards the automatic detection of adventitious lung sounds ofBronchiectasis,COPD from normal lung sounds of healthy subjects. For classification of the lung sounds into a normaland adventitious category, we obtain features from phase space representation (PSR). At first, the empirical mode decomposition (EMD) is applied to lung sound signals to obtain intrinsic mode functions (IMFs). The IMFs are then further processed to construct two dimensional (2D) and three dimensional (3D) PSR. The feature space includes the 95% confidence ellipse area and interquartile range (IQR) of Euclidian distances computed from 2D and 3D PSRs, respectively. The process is carried out for the first four IMFs correspondings to normal and adventitious lung sound signals. The computed features depicta significant ability to discriminate the two categories of lung sound signals.To perform classification, we use the least square support vector machine with two kernels, namely, polynomial and radial basis function (RBF).Simulation outcomes on ICBHI 2017 lung sound dataset show the ability of the proposed method in effectively classifying normal and adventitious lung sound signals. LS-SVM is employing RBF kernel provides the highest classification accuracy of 97.67 % over feature space constituted by first, second, and fourth IMF.

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