Literature DB >> 10988103

The relationship between normal lung sounds, age, and gender.

V Gross1, A Dittmar, T Penzel, F Schüttler, P von Wichert.   

Abstract

Auscultation is one of the most important noninvasive and feasible methods for the detection of lung diseases. Systematic changes in breathing sounds with increasing age are of diagnostic importance. To investigate these changes, we recorded lung sounds taken from four locations in the posterior thorax of 162 subjects, together with airflow. The data were analyzed according to age, sex, and smoking habit. In order to describe the power spectrum of the lung sounds, we calculated mean and median frequency, frequency with the highest power, and a ratio (Q) of relative power of the two frequency bands of 330 to 600 Hz and 60 to 330 Hz. Linear regression analysis was used as a measurement of age-dependence of these variables. Significant differences in Q were found in men versus women (p < 0.05), but not in smokers versus nonsmokers. Within the groups, a small but significant correlation existed between Q and age (r(2) </= 0.1, p < 0.05). For both men and women, a slight increase of the relative power in the frequency band of 330 to 600 Hz was recorded with increasing age. However, on the basis of large individual variations, these small changes (DeltaQ approximately 5%, SD(Q) >/= +/- 5%) have no clinical significance and need not to be considered in the automatic detection of lung diseases by analyzing lung sounds.

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Year:  2000        PMID: 10988103     DOI: 10.1164/ajrccm.162.3.9905104

Source DB:  PubMed          Journal:  Am J Respir Crit Care Med        ISSN: 1073-449X            Impact factor:   21.405


  21 in total

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Journal:  Respir Med       Date:  2011-06-14       Impact factor: 3.415

2.  Computerised respiratory sounds can differentiate smokers and non-smokers.

Authors:  Ana Oliveira; Ipek Sen; Yasemin P Kahya; Vera Afreixo; Alda Marques
Journal:  J Clin Monit Comput       Date:  2016-05-10       Impact factor: 2.502

3.  Early detection of deteriorating ventilation by monitoring bilateral chest wall dynamics in the rabbit.

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4.  A new method for continuous monitoring of chest wall movement to characterize hypoxemic episodes during HFOV.

Authors:  Dan Waisman; Carmit Levy; Anna Faingersh; Fatmi Ifat Colman Klotzman; Eugene Konyukhov; Irena Kessel; Avi Rotschild; Amir Landesberg
Journal:  Intensive Care Med       Date:  2011-04-29       Impact factor: 17.440

5.  A comparison of the power of breathing sounds signals acquired with a smart stethoscope from a cohort of COVID-19 patients at peak disease, and pre-discharge from the hospital.

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Journal:  Biomed Signal Process Control       Date:  2022-06-27       Impact factor: 5.076

6.  Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope.

Authors:  Valerie Rennoll; Ian McLane; Dimitra Emmanouilidou; James West; Mounya Elhilali
Journal:  IEEE J Biomed Health Inform       Date:  2021-05-11       Impact factor: 5.772

7.  Computer-aided diagnosis of pneumonia in patients with chronic obstructive pulmonary disease.

Authors:  Daniel Sánchez Morillo; Antonio León Jiménez; Sonia Astorga Moreno
Journal:  J Am Med Inform Assoc       Date:  2013-02-08       Impact factor: 4.497

Review 8.  Acoustic Methods for Pulmonary Diagnosis.

Authors:  Adam Rao; Emily Huynh; Thomas J Royston; Aaron Kornblith; Shuvo Roy
Journal:  IEEE Rev Biomed Eng       Date:  2018-10-29

9.  Effect of airflow rate on vibration response imaging in normal lungs.

Authors:  Meirav Yosef; Ruben Langer; Shaul Lev; Yael A Glickman
Journal:  Open Respir Med J       Date:  2009-09-17

10.  Evaluation of Vibration Response Imaging (VRI) Technique and Difference in VRI Indices Among Non-Smokers, Active Smokers and Passive Smokers.

Authors:  Hongying Jiang; Jichao Chen; Jinying Cao; Lan Mu; Zhenyu Hu; Jian He
Journal:  Med Sci Monit       Date:  2015-07-27
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