Normal Versus Adventitious Respiratory Sounds

The development of graphical user interfaces (GUIs) has been an emergent demand in the area of healthcare technologies. Specifically for respiratory healthcare there is a lack of tools to produce a complete multimedia database, where respiratory sounds and other clinical data are available in a single repository. This is essential for a complete patients' assessment and management in research/clinical settings. Therefore, this study aimed to develop a usable interface to collect and organise respiratory-related data in a single multimedia database. A GUI, named LungSounds@UA, composed by a multilayer of windows, was developed. The usability of the user-centred interface was assessed in a pilot study and in an evaluation session. The users testified the utility of the application and its great potential for research/clinical settings. However, some drawbacks were identified, such as a certain difficulty to intuitively navigate in the great amount of the available information, which will inform future developments.

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A Virtual System for Measurements and Analysis of the Respiratory Sounds for Diagnosis of Respiratory System

Proceedings of International Conference on Artificial Life and Robotics ◽

10.5954/icarob.2020.gs6-9 ◽

2020 ◽

Vol 25 ◽

pp. 87-93

Author(s):

Ali AlMejrad

Keyword(s):

Respiratory System ◽

Respiratory Sounds ◽

Virtual System

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Classification of Lung Sounds and Disease Prediction using Dense CNN Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a3207.1011121 ◽

2021 ◽

Vol 11 (1) ◽

pp. 195-198

Author(s):

Suyash Lakhani ◽

◽

Ridhi Jhamb ◽

Keyword(s):

Respiratory Sounds ◽

Respiratory Sound ◽

Lung Sounds ◽

Dense Network ◽

Lung Sound ◽

Respiratory Dysfunction ◽

Respiratory Disorders ◽

Respiratory Illnesses ◽

In The Beginning

Respiratory illnesses are a main source of death in the world and exact lung sound identification is very significant for the conclusion and assessment of sickness. Be that as it may, this method is vulnerable to doctors and instrument limitations. As a result, the automated investigation and analysis of respiratory sounds has been a field of great research and exploration during the last decades. The classification of respiratory sounds has the potential to distinguish anomalies and diseases in the beginning phases of a respiratory dysfunction and hence improve the accuracy of decision making. In this paper, we explore the publically available respiratory sound database and deploy three different convolutional neural networks (CNN) and combine them to form a dense network to diagnose the respiratory disorders. The results demonstrate that this dense network classifies the sounds accurately and diagnoses the corresponding respiratory disorders associated with them.

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Tele-auscultation of respiratory sounds with a low-cost digital stethoscope

Eurasian Journal of Pulmonology ◽

10.4103/ejop.ejop_49_19 ◽

2019 ◽

Vol 21 (3) ◽

pp. 212

Author(s):

Himel Mondal ◽

Shaikat Mondal

Keyword(s):

Low Cost ◽

Respiratory Sounds

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Respiratory sound recorder – continued Innovation

Pediatrics (Suppl Consilium Medicum) ◽

10.26442/26586630.2021.2.201017 ◽

2021 ◽

pp. 146-151

Author(s):

Lada S. Starostina ◽

Natalia A. Geppe ◽

Vladimir S. Malyshev ◽

Saniia I. Valieva ◽

Irina L. Ginesina ◽

...

Keyword(s):

Respiratory Tract ◽

Computer Technology ◽

Respiratory Function ◽

Age Groups ◽

Patient Treatment ◽

Respiratory Sounds ◽

Early Age ◽

Respiratory Sound ◽

Respiratory Noises ◽

Inner Diameter

The study of external respiratory function (ERF) is important in the diagnosis of respiratory tract abnormalities in various diseases. In children, especially at an early age, there are many difficulties in conducting studies. In recent decades, due to the development of computer technology, there is great interest in the study of respiratory sounds, methods of their registration, processing and use in the assessment of the respiratory system in children and adults. Russian scientists have developed the method of respiratory airway sound investigation, which has proved its effectiveness, reliability and necessity of use in practice. Computer bronchophonography is based on the analysis of time and frequency characteristics of the spectrum of respiratory noises, arising from changes in the bronchial diameter due to increase in the stiffness of their walls or decrease in the inner diameter. Computed bronchophonography may be used for diagnostics of EFD disorders in patients of all age groups both in the in-patient and out-patient treatment.

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The Remote Analysis of Breath Sound in COVID-19 Patients: A Series of Clinical cases (Preprint)

10.2196/preprints.31200 ◽

2021 ◽

Author(s):

Gregory Furman

Keyword(s):

Computer Analysis ◽

High Frequency ◽

Sampling Rate ◽

Respiratory Sounds ◽

Computer Methods ◽

Computer Assistance ◽

Self Testing ◽

Gender And Age ◽

Diagnostic Values ◽

Remote Analysis

BACKGROUND Respiratory sounds have been recognized as a possible indicator of behavior and health. Computer analysis of these sounds can indicate of characteristic sound changes caused by COVID-19 and can be used for diagnosis of this illness OBJECTIVE The communication aim is development of fast remote computer-assistance diagnosis methods for COVID-19, based on analysis of respiratory sounds METHODS Fast Fourier transform (FFT) was applied for computer analysis of respiratory sounds recorded near the mouth of 14 COVID-19 patients (age 18-80) and 17 healthy volunteers (age from 5 to 48). Sampling rate was from 44 to 96 kHz. Unlike usual computer-assistance methods of diagnostics of illness, based on respiratory sound analysis, we propose to test the high frequency part of the FFT spectrum (2000-6000 Hz). RESULTS Comparing FFT spectrums of the respiratory sounds of the patients and volunteers we developed computer-assistance methods of COVID 19 diagnostics and determined numerical healthy-ill criterions. These criterions are independent of gender and age of the tested person. CONCLUSIONS The proposed computer methods, based on analysis of the FFT spectrums of respiratory sounds of the patients and volunteers, allows one to automatically diagnose COVID-19 with sufficiently high diagnostic values. These methods can be applied to develop noninvasive self-testing kits for COVID-19.

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