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 Comparison of analogue and electronic stethoscopes for pulmonary auscultation by internal medicine residents

2018 ◽  
Vol 94 (1118) ◽  
pp. 700-703
Author(s):  
Eric R. Gottlieb ◽  
Jason M. Aliotta ◽  
Dominick Tammaro
Keyword(s):  
Internal Medicine ◽  
Clinical Decision Making ◽  
Statistical Significance ◽  
Internal Medicine Resident ◽  
Clinical Decision ◽  
P Value ◽  
Lung Sounds ◽  
Electronic Stethoscope ◽  
Medicine Resident ◽  
Internal Medicine Residents

BackgroundElectronic stethoscopes are becoming more common in clinical practice. They may improve the accuracy and efficiency of pulmonary auscultation, but the data to support their benefit are limited.ObjectiveTo determine how auscultation with an electronic stethoscope may affect clinical decision making.MethodsAn online module consisting of six fictional ambulatory cases was developed. Each case included a brief history and lung sounds recorded with an analogue and electronic stethoscope. Internal medicine resident participants were randomly selected to hear either the analogue or electronic lung sounds. Numbers of correct answers, time spent on each case and numbers of times the recordings were played were compared between the groups who heard each mode of auscultation, with a p value of less than 0.05 indicating statistical significance.Results61 internal medicine residents completed at least one case, and 41 residents completed all six cases. There were no significant differences in overall scores between participants who heard analogue and electronic lung sounds (3.14±0.10 out of 6 correct for analogue, 3.20±0.10 out of 6 for electronic, p=0.74). There were no significant differences in performance for any of the six cases (p=0.78), time spent on the cases (p=0.67) or numbers of times the recordings were played (p=0.85).ConclusionWhen lung sounds were amplified with an electronic stethoscope, we did not detect an effect on performance, time spent on the cases or numbers of times participants listened to the recordings.

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.

scholarly journals The Utility of Real-Time Remote Lung Auscultation Using a Bluetooth-Connected Electronic Stethoscope: Open-Label Randomized Controlled Pilot Trial (Preprint)

2020 ◽  
Author(s):  
Takanobu Hirosawa ◽  
Yukinori Harada ◽  
Kohei Ikenoya ◽  
Shintaro Kakimoto ◽  
Yuki Aizawa ◽  
...  
Keyword(s):  
Real Time ◽  
Test Score ◽  
Intervention Group ◽  
Control Group ◽  
Lung Sounds ◽  
Lung Sound ◽  
Open Label ◽  
Electronic Stethoscope ◽  
Randomized Controlled ◽  
Lung Simulator

BACKGROUND The urgent need for telemedicine has become clear situation in the pandemic of the coronavirus disease 2019. To facilitate telemedicine, the development and improvement of remote examination systems are required. A system combining an electronic stethoscope and Bluetooth connectivity is a promising option for remote auscultation in clinics and hospitals. However, the utility of such systems remains unknown. OBJECTIVE This study was conducted to assess the utility of real-time auscultation, using a Bluetooth-connected electronic stethoscope compared to that of classical auscultation, using a lung simulator. METHODS This was an open-label randomized controlled trial, including senior residents and faculty in the department of general internal medicine of a university hospital. The only exclusion criterion was a refusal to participate. All participants attended a tutorial session, in which they listened to 15 lung sounds on the lung simulator using a classic stethoscope and were told the correct classification. Thereafter, participants were randomly assigned to either the real-time remote auscultation group (intervention group) or the classical auscultation group (control group), for test sessions. In the test sessions, participants had to classify a series of ten lung sounds. The intervention group listened to the lung sounds remotely, using the electronic stethoscope, a Bluetooth transmitter, and a wireless, noise-canceling, stereo headset. The control group listened to the lung sounds directly using a traditional stethoscope. The primary outcome was the test score, and the secondary outcomes were the rates of correct answers for each lung sound. The two groups were compared using the Fisher exact test. RESULTS In total, 20 participants were included; eleven and nine were assigned to the intervention and control groups, respectively. There was no difference in age (P=.25), sex (P=.82), and years from graduation (P=.15) between the two groups. The overall test score in the intervention group (80/110, 72.7%) was not different from that in the control group (71/90, 78.9%) (P=.32). The only lung sound for which the correct answer rate differed between groups was that of pleural friction rubs (P=.03); it was lower in the intervention group (3/11, 27%) than in the control group (7/9, 78%,). CONCLUSIONS The utility of a real-time remote auscultation system using a Bluetooth-connected electronic stethoscope was comparable to that of direct auscultation using a classic stethoscope, except for classification of pleural friction rubs. CLINICALTRIAL UMIN-CTR UMIN000040828; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046222.

scholarly journals Preliminary Detection of Lung Diseases in Pediatric Population using Soft Computing

2019 ◽  
Vol 8 (3) ◽  
pp. 2731-2735
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lung Diseases ◽  
Human Subjects ◽  
Pediatric Population ◽  
Lung Sounds ◽  
Electronic Stethoscope ◽  
Temporal Features ◽  
Lung Abnormalities ◽  
Hidden Layer

The investigations from recent studies clearly show the potential of lung sounds in detection of lung abnormalities in human subjects. This paper aims to analyze lung sounds acquired using special electronic stethoscope for detection adventitious sounds arising out of pathological lungs due to various disease like brochities especially in pediatric population. For acquisition and recording of lung sounds, 3M Littmann 3200 model is utilized. After verifying fidelity of electronic stethoscope, the analysis of lung sounds was carried out by various spectral and temporal features. The features extracted were fed to artificial neural network for classification. Various combinations of ANN with different topologies were experimented. The overall accuracy of obtained with one hidden layer GFF is 94.95%.

Modified chest wall reconstruction after sternum necrosis in cardiac surgery patients

2014 ◽  
Vol 62 (S 01) ◽  
Author(s):  
L. Tewarie ◽  
A.K. Moza ◽  
A. Goetzenich ◽  
R. Zayat ◽  
R. Autschbach
Keyword(s):  
Cardiac Surgery ◽  
Chest Wall ◽  
Chest Wall Reconstruction
Sign in / Sign up

Export Citation Format

Share Document