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Journal ArticleDOI

Automated Spectral Characterization of Wheezing in Asthmatic Children

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TLDR
Wheezing was found to be strongly dependent upon air flow, and generally followed the changes in pulmonary function as indicated by the forced expiratory volume at 1 s (FEV1).
Abstract
Breath sounds were recorded in normal and asthmatic children over the chest and trachea. The power spectra of the sounds were analyzed for peaks of high amplitude and high frequency as indications of wheezing. The percent of inspiration and expiration spent wheezing was used as an indication of the severity of bronchial obstruction. Wheezing was found to be strongly dependent upon air flow, and generally followed the changes in pulmonary function as indicated by the forced expiratory volume at 1 s (FEV1). The trachea was found to be a better location for analyzing wheezes than the lung.

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Citations
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Classification of Respiratory Sounds Based on Wavelet Packet Decomposition

TL;DR: A wavelet packet-based method is used for detection of abnormal respiratory sounds and the preliminary results are promising, although not yet good enough for clinical use.
Journal ArticleDOI

Nomenclature Used by Health Care Professionals to Describe Breath Sounds in Asthma

TL;DR: The observed variability in spontaneous, uninstructed description of breath sounds in asthmatic patients indicates the importance of teaching a standardized nomenclature for lung sounds to health care professionals, using only terms which are clearly informative of pulmonary disease.
Posted Content

A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram

TL;DR: This work proposes a lightweight convolutional neural network architecture to classify respiratory diseases from individual breath cycles using hybrid scalogram-based features of lung sounds, which outperforms well-known and much larger VGG16 in terms of accuracy by absolute margins.
Journal ArticleDOI

Automatic Wheezing Detection Based on Signal Processing of Spectrogram and Back-Propagation Neural Network.

TL;DR: A wheezing detection algorithm based on the order truncate average method and a back-propagation neural network (BPNN) is proposed, which shows a high sensitivity and high specificity for wheeze recognition.
Journal ArticleDOI

The need for standards in recording and analysing respiratory sounds

TL;DR: Although the review cannot propose immediately acceptable guidelines and standards for RS analysis, it proposes a ‘seed’ set of guidelines that are ‘up for discussion’ between investigators in the field, the final goal being to inject a degree of standardisation in equipment and methods that are acceptable to all involved.
References
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Journal ArticleDOI

Wave-speed limitation on expiratory flow-a unifying concept.

TL;DR: The theoretical approach to the "waterfall effect" leads to selection of the analogy of constricted open-channel flow to apply to the elastic network of airway tubes, and results are derived for the case of negligible friction by use of the Bernoulli principle.
Journal ArticleDOI

Spectral characteristics of normal breath sounds.

TL;DR: Breath sounds picked up over the trachea were characterized by power spectra typical to a broad spectrum sound with a sharp decrease of power at a cut-off frequency that varied between 850 and 1,600 Hz among the 10 healthy subjects studied.
Journal ArticleDOI

Visual Lung-Sound Characterization by Time-Expanded Wave-Form Analysis

TL;DR: Time-expanded wave form analysis provides reproducible visual displays that allow documentation of the differentiating features of lung sounds and enhances the diagnostic utility of the sounds.
Journal ArticleDOI

Relationship of Wheezing to the Severity of Obstruction in Asthma

TL;DR: Although characterization of wheezing has a general relationship to the severity of airway obstruction, an objective measurement of expiratory flow rate is necessary for the evaluation of each patient's condition.
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