Detection of Respiratory Crackle Sounds via an Android Smartphone-based System

Olvera-Montes, Nemecio; Reyes, Bersaín A.; Charleston-Villalobos, S.; González-Camarena, R.; Mejía-Ávila, Mayra Edith; Dorantes-Méndez, Guadalupe; Reulecke, Sina

doi:10.1109/embc.2018.8512672

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Complementing this information with limitations pointed out in the open answer question, the major concern for the patients was the lack of physical examination, so it can be hypothesized that this is the main factor preventing further acceptance to this telemedicine tool. Some smartphone apps have been tested for the detection and analysis of both cardiac and pulmonary auscultation sounds, and might constitute a future solution to overcome this limitation of virtual visits (15,16).…”

Section: Discussionmentioning

confidence: 99%

What do asthmatic patients think about telemedicine visits?

Sousa

Barbosa

Aguiar

et al. 2021

Eur Ann Allergy Clin Immunol

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Section: Discussionmentioning

confidence: 99%

What do asthmatic patients think about telemedicine visits?

Sousa

Barbosa

Aguiar

et al. 2021

Eur Ann Allergy Clin Immunol

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“…Automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and pneumonia [ 14 ]. Olvera-Montes et al [ 15 ] used the detection of respiratory crackle sounds through an Android smartphone–based system for the diagnostics of pneumonia and monitoring of the patient’s state.…”

Section: Introductionmentioning

confidence: 99%

Remote Analysis of Respiratory Sounds in Patients With COVID-19: Development of Fast Fourier Transform–Based Computer-Assisted Diagnostic Methods

Furman¹,

Furman²,

Charushin³

et al. 2022

JMIR Form Res

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Background Respiratory sounds have been recognized as a possible indicator of behavior and health. Computer analysis of these sounds can indicate characteristic sound changes caused by COVID-19 and can be used for diagnostics of this illness. Objective The aim of the study is to develop 2 fast, remote computer-assisted diagnostic methods for specific acoustic phenomena associated with COVID-19 based on analysis of respiratory sounds. Methods Fast Fourier transform (FFT) was applied for computer analysis of respiratory sound recordings produced by hospital doctors near the mouths of 14 patients with COVID-19 (aged 18-80 years) and 17 healthy volunteers (aged 5-48 years). Recordings for 30 patients and 26 healthy persons (aged 11-67 years, 34, 60%, women), who agreed to be tested at home, were made by the individuals themselves using a mobile telephone; the records were passed for analysis using WhatsApp. For hospitalized patients, the illness was diagnosed using a set of medical methods; for outpatients, polymerase chain reaction (PCR) was used. The sampling rate of the recordings was from 44 to 96 kHz. Unlike usual computer-assisted diagnostic methods for illnesses based on respiratory sound analysis, we proposed to test the high-frequency part of the FFT spectrum (2000-6000 Hz). Results Comparing the FFT spectra of the respiratory sounds of patients and volunteers, we developed 2 computer-assisted methods of COVID-19 diagnostics and determined numerical healthy-ill criteria. These criteria were independent of gender and age of the tested person. Conclusions The 2 proposed computer-assisted diagnostic methods, based on the analysis of the respiratory sound FFT spectra of patients and volunteers, allow one to automatically diagnose specific acoustic phenomena associated with COVID-19 with sufficiently high diagnostic values. These methods can be applied to develop noninvasive screening self-testing kits for COVID-19.

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“…The automated adventitious sound detection or/and their classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases, such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia [9]. Nemecio Olvera-Montes and co-authors [10] used the detection of respiratory crackle sounds via an android smartphone-based system for diagnostics of pneumonia and monitoring of the patient state.…”

Section: Introductionmentioning

confidence: 99%

The Remote Analysis of Breath Sound in Covid-19 Patients: A Series of Clinical Cases

Charushin

Эйрих

et al. 2020

Preprint

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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. Purpose: The communication aim is development of fast remote computer-assistance diagnosis of COVID-19, based on analysis of respiratory sounds. Materials and Methods: Fast Fourier transform (FFT) was applied for analyses of respiratory sounds recorded near the mouth of 9 COVID-19 patients and 4 healthy volunteers. Sampling rate was 48 kHz. Results: Comparing of FFT spectrums of the respiratory sounds of the patients and volunteers we proposed numerical healthy-ill criterions. Conclusions: The proposed computer method, 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. This method can be applied at development of noninvasive self-testing kits for COVID-19.

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Detection of Respiratory Crackle Sounds via an Android Smartphone-based System

Cited by 8 publications

References 7 publications

What do asthmatic patients think about telemedicine visits?

What do asthmatic patients think about telemedicine visits?

Remote Analysis of Respiratory Sounds in Patients With COVID-19: Development of Fast Fourier Transform–Based Computer-Assisted Diagnostic Methods

The Remote Analysis of Breath Sound in Covid-19 Patients: A Series of Clinical Cases

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