Untangling the graph based features for lung sound auscultation

2022 ◽  
Vol 71 ◽  
pp. 103215
Author(s):  
S. Sankararaman
Keyword(s):  
Lung Sound

scholarly journals Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1393
Author(s):  
Uduak Z. George ◽  
Kee S. Moon ◽  
Sung Q. Lee
Keyword(s):  
Tidal Volume ◽  
Respiration Rate ◽  
Spline Interpolation ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Lung Sound ◽  
Health Monitoring System ◽  
Electrocardiogram Ecg

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

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.

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.

Lung Sound Analysis for Continuous Evaluation of Airflow Obstruction in Asthma

CHEST Journal ◽  
1985 ◽  
Vol 88 (3) ◽  
pp. 364-368 ◽  
Author(s):  
Robert P. Baughman ◽  
Robert G. Loudon
Keyword(s):  
Airflow Obstruction ◽  
Lung Sound ◽  
Sound Analysis ◽  
Continuous Evaluation

An Analysis of Lung Sound from Electronic Stethoscope with Spectrogram

2019 ◽  
pp. 107-113
Author(s):  
Trinh Quang Duc ◽  
Nguyen Van Son ◽  
Nguyen Hoai Giang ◽  
Dao Huy Du ◽  
Ha Ngoc Thu
Keyword(s):  
Lung Sound ◽  
Electronic Stethoscope

scholarly journals Survey of Adventitious Lung Sound Terminology

1990 ◽  
Vol 37 (3) ◽  
pp. 239-247
Author(s):  
Sung Kyu Kim
Keyword(s):  
Lung Sound

scholarly journals Classification of Lung Sounds and Disease Prediction using Dense CNN Network

2021 ◽  
Vol 11 (1) ◽  
pp. 195-198
Author(s):  
Suyash Lakhani ◽  
◽  
Ridhi Jhamb ◽  
Keyword(s):  
Respiratory Sounds ◽  
Respiratory Sound ◽  
Lung Sounds ◽  
Dense Network ◽  
Lung Sound ◽  
Respiratory Dysfunction ◽  
Respiratory Disorders ◽  
Respiratory Illnesses ◽  
In The Beginning

Respiratory illnesses are a main source of death in the world and exact lung sound identification is very significant for the conclusion and assessment of sickness. Be that as it may, this method is vulnerable to doctors and instrument limitations. As a result, the automated investigation and analysis of respiratory sounds has been a field of great research and exploration during the last decades. The classification of respiratory sounds has the potential to distinguish anomalies and diseases in the beginning phases of a respiratory dysfunction and hence improve the accuracy of decision making. In this paper, we explore the publically available respiratory sound database and deploy three different convolutional neural networks (CNN) and combine them to form a dense network to diagnose the respiratory disorders. The results demonstrate that this dense network classifies the sounds accurately and diagnoses the corresponding respiratory disorders associated with them.

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