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High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone

In this paper, a smartphone-based lung function test, developed to estimate lung function parameters using a high-resolution time-frequency spectrum from a smartphone built-in microphone is presented. A method of estimation of the forced expiratory volume in 1 s divided by forced vital capacity (FEV...

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Autores principales: Thap, Tharoeun, Chung, Heewon, Jeong, Changwon, Hwang, Ki-Eun, Kim, Hak-Ryul, Yoon, Kwon-Ha, Lee, Jinseok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017470/
https://www.ncbi.nlm.nih.gov/pubmed/27548164
http://dx.doi.org/10.3390/s16081305
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author Thap, Tharoeun
Chung, Heewon
Jeong, Changwon
Hwang, Ki-Eun
Kim, Hak-Ryul
Yoon, Kwon-Ha
Lee, Jinseok
author_facet Thap, Tharoeun
Chung, Heewon
Jeong, Changwon
Hwang, Ki-Eun
Kim, Hak-Ryul
Yoon, Kwon-Ha
Lee, Jinseok
author_sort Thap, Tharoeun
collection PubMed
description In this paper, a smartphone-based lung function test, developed to estimate lung function parameters using a high-resolution time-frequency spectrum from a smartphone built-in microphone is presented. A method of estimation of the forced expiratory volume in 1 s divided by forced vital capacity (FEV(1)/FVC) based on the variable frequency complex demodulation method (VFCDM) is first proposed. We evaluated our proposed method on 26 subjects, including 13 healthy subjects and 13 chronic obstructive pulmonary disease (COPD) patients, by comparing with the parameters clinically obtained from pulmonary function tests (PFTs). For the healthy subjects, we found that an absolute error (AE) and a root mean squared error (RMSE) of the FEV(1)/FVC ratio were 4.49% ± 3.38% and 5.54%, respectively. For the COPD patients, we found that AE and RMSE from COPD patients were 10.30% ± 10.59% and 14.48%, respectively. For both groups, we compared the results using the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and found that VFCDM was superior to CWT and STFT. Further, to estimate other parameters, including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV(1)), and peak expiratory flow (PEF), regression analysis was conducted to establish a linear transformation. However, the parameters FVC, FEV1, and PEF had correlation factor r values of 0.323, 0.275, and −0.257, respectively, while FEV(1)/FVC had an r value of 0.814. The results obtained suggest that only the FEV1/FVC ratio can be accurately estimated from a smartphone built-in microphone. The other parameters, including FVC, FEV1, and PEF, were subjective and dependent on the subject’s familiarization with the test and performance of forced exhalation toward the microphone.
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spelling pubmed-50174702016-09-22 High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone Thap, Tharoeun Chung, Heewon Jeong, Changwon Hwang, Ki-Eun Kim, Hak-Ryul Yoon, Kwon-Ha Lee, Jinseok Sensors (Basel) Article In this paper, a smartphone-based lung function test, developed to estimate lung function parameters using a high-resolution time-frequency spectrum from a smartphone built-in microphone is presented. A method of estimation of the forced expiratory volume in 1 s divided by forced vital capacity (FEV(1)/FVC) based on the variable frequency complex demodulation method (VFCDM) is first proposed. We evaluated our proposed method on 26 subjects, including 13 healthy subjects and 13 chronic obstructive pulmonary disease (COPD) patients, by comparing with the parameters clinically obtained from pulmonary function tests (PFTs). For the healthy subjects, we found that an absolute error (AE) and a root mean squared error (RMSE) of the FEV(1)/FVC ratio were 4.49% ± 3.38% and 5.54%, respectively. For the COPD patients, we found that AE and RMSE from COPD patients were 10.30% ± 10.59% and 14.48%, respectively. For both groups, we compared the results using the continuous wavelet transform (CWT) and short-time Fourier transform (STFT), and found that VFCDM was superior to CWT and STFT. Further, to estimate other parameters, including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV(1)), and peak expiratory flow (PEF), regression analysis was conducted to establish a linear transformation. However, the parameters FVC, FEV1, and PEF had correlation factor r values of 0.323, 0.275, and −0.257, respectively, while FEV(1)/FVC had an r value of 0.814. The results obtained suggest that only the FEV1/FVC ratio can be accurately estimated from a smartphone built-in microphone. The other parameters, including FVC, FEV1, and PEF, were subjective and dependent on the subject’s familiarization with the test and performance of forced exhalation toward the microphone. MDPI 2016-08-17 /pmc/articles/PMC5017470/ /pubmed/27548164 http://dx.doi.org/10.3390/s16081305 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Thap, Tharoeun
Chung, Heewon
Jeong, Changwon
Hwang, Ki-Eun
Kim, Hak-Ryul
Yoon, Kwon-Ha
Lee, Jinseok
High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone
title High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone
title_full High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone
title_fullStr High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone
title_full_unstemmed High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone
title_short High-Resolution Time-Frequency Spectrum-Based Lung Function Test from a Smartphone Microphone
title_sort high-resolution time-frequency spectrum-based lung function test from a smartphone microphone
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017470/
https://www.ncbi.nlm.nih.gov/pubmed/27548164
http://dx.doi.org/10.3390/s16081305
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