Respiratory sounds in healthy people: A systematic review

Oliveira, Ana; Marques, Alda

doi:10.1016/j.rmed.2014.01.004

Cited by 61 publications

(35 citation statements)

References 36 publications

(108 reference statements)

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“…Such findings are related with the low number of crackles presented (TP) in these two respiratory sound files (1 9 crackles in the sound file), causing the few FP and FN to have a negative impact in the F-index. Nevertheless, this error might not have relevance for clinical practice as it has been reported that healthy people present approximately 4 crackles per breathing cycle 27 . Hence errors of this magnitude might not be clinically significant for diagnosis and monitoring of respiratory diseases.…”

Section: Discussionmentioning

confidence: 97%

Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering

Pinho¹,

Oliveira²,

Jácome³

et al. 2015

Procedia Computer Science

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Crackles are adventitious respiratory sounds that provide valuable information on different respiratory conditions. Crackles automatic detection in a respiratory sound file is challenging, and thus different signal processing methodologies have been proposed. However, limited testing of such methodologies, namely in respiratory sound files collected in clinical settings, has been conducted. This study aimed to develop an algorithm for automatic crackle detection and characterisation and to evaluate its performance and accuracy against a multi-annotator gold standard. The algorithm is based on three main procedures: i) extraction of a window of interest of a potential crackle (based on fractal dimension and box filtering techniques); ii) verification of the validity of the potential crackle considering computerised respiratory sound analysis established criteria; and iii) characterisation and extraction of crackle parameters. Twenty four 10-second files, acquired in clinical settings, were selected from 10 patients with pneumonia and cystic fibrosis. The algorithm performance was assessed by comparing its results with gold standard annotations (obtained by the agreement among three experts). A set of 7 parameters was optimised. High levels of sensitivity (SE=89%), positive predictive value (PPV=95%) and overall performance (F index=92%) were achieved. This promising result highlights the potential of the algorithm for automatic crackle's detection/characterisation in respiratory sounds acquired in clinical settings.

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Section: Discussionmentioning

confidence: 97%

Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering

Pinho¹,

Oliveira²,

Jácome³

et al. 2015

Procedia Computer Science

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“…30 Interestingly, the authors of a recent systematic review also found wheezing in 1%-5% and crackles in 7%-37% of healthy, asymptomatic adults from studies in the USA, but noted a gap in paediatric data, as there were no published studies that included healthy children. 31 Second, our expert listeners were blinded to the case-control status and also the visual cues that exist during a live patient encounter, and this may have increased false positive examinations; by looking at and listening to the patient at the same time, visual cues can help a clinician distinguish between wheezes, upper airway sounds such as cries, normal vocalisations, or transmitted nasal congestion (ie, the clinician can see the child crying, vocalising or rhinorrhea), or crackles and movement artefact (ie, the clinician can see the child moving), all of which can overlap in their amplitude and frequency profiles. 32 Notably, the majority of control lung sounds were recorded from Thailand (58.8% (167/284)), as this was the only site to enrol all controls into the digital auscultation substudy.…”

Section: Discussionmentioning

confidence: 99%

Listening panel agreement and characteristics of lung sounds digitally recorded from children aged 1–59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case–control study

et al. 2017

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IntroductionPaediatric lung sound recordings can be systematically assessed, but methodological feasibility and validity is unknown, especially from developing countries. We examined the performance of acoustically interpreting recorded paediatric lung sounds and compared sound characteristics between cases and controls.MethodsPneumonia Etiology Research for Child Health staff in six African and Asian sites recorded lung sounds with a digital stethoscope in cases and controls. Cases aged 1–59 months had WHO severe or very severe pneumonia; age-matched community controls did not. A listening panel assigned examination results of normal, crackle, wheeze, crackle and wheeze or uninterpretable, with adjudication of discordant interpretations. Classifications were recategorised into any crackle, any wheeze or abnormal (any crackle or wheeze) and primary listener agreement (first two listeners) was analysed among interpretable examinations using the prevalence-adjusted, bias-adjusted kappa (PABAK). We examined predictors of disagreement with logistic regression and compared case and control lung sounds with descriptive statistics.ResultsPrimary listeners considered 89.5% of 792 case and 92.4% of 301 control recordings interpretable. Among interpretable recordings, listeners agreed on the presence or absence of any abnormality in 74.9% (PABAK 0.50) of cases and 69.8% (PABAK 0.40) of controls, presence/absence of crackles in 70.6% (PABAK 0.41) of cases and 82.4% (PABAK 0.65) of controls and presence/absence of wheeze in 72.6% (PABAK 0.45) of cases and 73.8% (PABAK 0.48) of controls. Controls, tachypnoea, >3 uninterpretable chest positions, crying, upper airway noises and study site predicted listener disagreement. Among all interpretable examinations, 38.0% of cases and 84.9% of controls were normal (p<0.0001); wheezing was the most common sound (49.9%) in cases.ConclusionsListening panel and case–control data suggests our methodology is feasible, likely valid and that small airway inflammation is common in WHO pneumonia. Digital auscultation may be an important future pneumonia diagnostic in developing countries.

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“…In a recent systematic review on respiratory sounds in healthy people, these methodological diff erences were also observed (36). Guidelines for research and clinical practice in the fi eld of respiratory sounds have been published in 2000 by the CORSA project group (34).…”

Section: Discussionmentioning

confidence: 99%

Computerized Respiratory Sounds in Patients with COPD: A Systematic Review

Jácome

Marques

2014

COPD: Journal of Chronic Obstructive Pulmonary Disease

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Computerized respiratory sound analysis provides objective information about the respiratory system and may be useful to monitor patients with chronic obstructive pulmonary disease (COPD) and detect exacerbations early. For these purposes, a thorough understanding of the typical computerized respiratory sounds in patients with COPD during stable periods is essential. This review aimed to systematize the existing evidence on computerized respiratory sounds in stable COPD. A literature search in the Medline, EBSCO, Web of Knowledge and Scopus databases was performed. Seven original articles were included. The maximum frequencies of normal inspiratory sounds at the posterior chest were between 113 and 130Hz, lower than the frequency found at trachea (228 Hz). During inspiration, the frequency of normal respiratory sounds was found to be higher than expiration (130 vs. 100Hz). Crackles were predominantly inspiratory (2.9-5 vs. expiratory 0.73-2) and characterized by long durations of the variables initial deflection width (1.88-2.1 ms) and two cycle duration (7.7-11.6 ms). Expiratory wheeze rate was higher than inspiratory rate. In patients with COPD normal respiratory sounds seem to follow the pattern observed in healthy people and adventitious respiratory sounds are mainly characterized by inspiratory and coarse crackles and expiratory wheezes. Further research with larger samples and following the Computerized Respiratory Sound Analysis (CORSA) guidelines are needed.

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Respiratory sounds in healthy people: A systematic review

Cited by 61 publications

References 36 publications

Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering

Automatic Crackle Detection Algorithm Based on Fractal Dimension and Box Filtering

Listening panel agreement and characteristics of lung sounds digitally recorded from children aged 1–59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case–control study

Computerized Respiratory Sounds in Patients with COPD: A Systematic Review

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