Parametric representation of normal breath sounds

1992 ◽  
Vol 73 (5) ◽  
pp. 1776-1784 ◽  
Author(s):  
N. Gavriely ◽  
M. Herzberg
Keyword(s):  
Chest Wall ◽  
Parametric Representation ◽  
Ar Model ◽  
Estimation Methods ◽  
Normal Lung ◽  
Lung Sounds ◽  
Cyclic Action ◽  
Breath Sounds ◽  
Tracheal Sounds ◽  
Ar Modeling

The spectral content of normal tracheal and chest wall breath sounds has been calculated using the fast Fourier transform (FFT) (J. Appl. Physiol. 50: 307–314, 1981). Parameter estimation methods, in particular autoregressive (AR) modeling, are alternative techniques for measuring lung sounds. The outcome of AR modeling of 38 complete breaths picked up simultaneously over the chest walls and tracheae of five normal males was evaluated. The sounds were treated as noise, bounded by a quasi-periodic envelope generated by the cyclic action of breathing, thus causing the sounds to become inherently nonstationary. Normalization of the sounds to their corresponding variance envelopes eliminated the nonstationarity, an important requirement for most signal-processing methods. Subsequently, the AR model order was sought using formal criteria. Orders 6–8 were found to be suitable for normal chest wall sounds, whereas tracheal sounds required at least orders 12–16. Using orders 6 and 12, we compared the prominent spectral features of chest wall and tracheal sounds calculated by AR with those found in the spectra calculated by FFT. The polar representation of the AR roots, calculated from the AR coefficients, showed that normal lung sounds from a group of individuals are characterized by a low variability, suggesting that this method may provide an alternative representation of the sounds. The data presented here show that normal lung sounds, when measured in the frequency domain by either FFT or AR modeling, have a characteristic pattern that is independent of the analysis method.

Measurement and theory of wheezing breath sounds

1984 ◽  
Vol 57 (2) ◽  
pp. 481-492 ◽  
Author(s):  
N. Gavriely ◽  
Y. Palti ◽  
G. Alroy ◽  
J. B. Grotberg
Keyword(s):  
Exponential Decay ◽  
Sound Production ◽  
Fluid Dynamic ◽  
Power Spectra ◽  
Helmholtz Resonator ◽  
Normal Lung ◽  
Lung Sounds ◽  
Frequency Range ◽  
Constant Frequency ◽  
Breath Sounds

We measured the time and frequency domain characteristics of breath sounds in seven asthmatic and three nonasthmatic wheezing patients. The power spectra of the wheezes were evaluated for frequency, amplitude, and timing of peaks of power and for the presence of an exponential decay of power with increasing frequency. Such decay is typical of normal vesicular breath sounds. Two patients who had the most severe asthma had no exponential decay pattern in their spectra. Other asthmatic patients had exponential patterns in some of their analyzed sound segments, with a range of slopes of the log power vs. log frequency curves from 5.7 to 17.3 dB/oct (normal range, 9.8–15.7 dB/oct). The nonasthmatic wheezing patients had normal exponential patterns in most of their analyzed sound segments. All patients had sharp peaks of power in many of the spectra of their expiratory and inspiratory lung sounds. The frequency range of the spectral peaks was 80–1,600 Hz, with some presenting constant frequency peaks throughout numerous inspiratory or expiratory sound segments recorded from one or more pickup locations. We compared the spectral shape, mode of appearance, and frequency range of wheezes with specific predictions of five theories of wheeze production: 1) turbulence-induced wall resonator, 2) turbulence-induced Helmholtz resonator, 3) acoustically stimulated vortex sound (whistle), 4) vortex-induced wall resonator, and 5) fluid dynamic flutter. We conclude that the predictions by 4 and 5 match the experimental observations better than the previously suggested mechanisms. Alterations in the exponential pattern are discussed in view of the mechanisms proposed as underlying the generation and transmission of normal lung sounds. The observed changes may reflect modified sound production in the airways or alterations in their attenuation when transmitted to the chest wall through the hyperinflated lung.

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

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.

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.

scholarly journals Spectral characteristics of chest wall breath sounds in normal subjects.

Thorax ◽  
1995 ◽  
Vol 50 (12) ◽  
pp. 1292-1300 ◽  
Author(s):  
N Gavriely ◽  
M Nissan ◽  
A H Rubin ◽  
D W Cugell
Keyword(s):  
Chest Wall ◽  
Spectral Characteristics ◽  
Normal Subjects ◽  
Breath Sounds

A New Algorithm for Long-Term Estimation Based on AR Model

2014 ◽  
Vol 614 ◽  
pp. 440-443 ◽  
Author(s):  
Wen Jun Su ◽  
Hai Tao Chen
Keyword(s):  
Spectral Density ◽  
Poor Performance ◽  
Ar Model ◽  
Estimation Methods ◽  
Wiener Model ◽  
Training Set ◽  
Power Spectral ◽  
Cross Spectral Density ◽  
Term Data

Traditional estimation methods have poor performance for long-term data forecast. Using Wiener model to estimate, power spectral density of the input signal, and cross-spectral density of the input and output signals are needed, that are difficult to obtain. And the large amount of calculation is needed using Wiener model. Using AR model and Kalman model, estimated results tend to mean of the training set while the estimated distance increases. For these cases, a new algorithm for long-term estimation based on AR model, named sampling AR model, is presented. Grouping the training set and using a different group of the training set to estimate each value. Sampling AR model improves the accuracy of long-term estimation.

SEMG Analysis Basing on AR Model and Bayes Taxonomy

2010 ◽  
Vol 44-47 ◽  
pp. 3355-3359 ◽  
Author(s):  
Guang Ying Yang ◽  
San Xiu Wang ◽  
Yue Chen
Keyword(s):  
Virtual Instrument ◽  
Recognition Rate ◽  
Ar Model ◽  
Semg Signal ◽  
Bayes Statistics ◽  
Emg Signal ◽  
Auto Regressive ◽  
Error Recognition ◽  
Ar Modeling ◽  
The Cost

This paper introduced a pattern recognition method based on auto-regression (AR) model and bayes taxonomy. The proposed methodology consists of three steps. In the first step, the paper designs a circuit to collect surface electromyography (SEMG) signal. In the second step, Auto-regressive (AR) modeling in time series has been applied on people’s forearm muscle. So, EMG signal is preprocessed using AR-Model to extract features from MES. After calculated the coefficients of and AR model, we distill the AR coefficients as its eigenvector. In the third step, a bayes statistics algorithm is designed to classify the muscle movement of forearm. This paper finds this method has many advantages such as reducing error recognition rate and has a relative good result. It proves that there are some relations between motion pattern and AR coefficients. At the same time, this paper adopts virtual instrument technology to raise accuracy of measurement, reduce the cost and workload.

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