Tracheal Sounds Acquisition Using Smartphones
Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds...
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-07-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/8/13830 |
| _version_ | 1798035056658219008 |
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| author | Bersain A. Reyes Natasa Reljin Ki H. Chon |
| author_facet | Bersain A. Reyes Natasa Reljin Ki H. Chon |
| author_sort | Bersain A. Reyes |
| collection | DOAJ |
| description | Tracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds’ amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r2 = 0.9693, 0.11 (−1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r2 = 0.9672, 0.097 (–1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found. |
| first_indexed | 2024-04-11T20:52:54Z |
| format | Article |
| id | doaj.art-86ace15cc88d46ee938e5edefe3431cf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:52:54Z |
| publishDate | 2014-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-86ace15cc88d46ee938e5edefe3431cf2022-12-22T04:03:48ZengMDPI AGSensors1424-82202014-07-01148138301385010.3390/s140813830s140813830Tracheal Sounds Acquisition Using SmartphonesBersain A. Reyes0Natasa Reljin1Ki H. Chon2Department of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USADepartment of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USADepartment of Biomedical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USATracheal sounds have received a lot of attention for estimating ventilation parameters in a non-invasive way. The aim of this work was to examine the feasibility of extracting accurate airflow, and automating the detection of breath-phase onset and respiratory rates all directly from tracheal sounds acquired from an acoustic microphone connected to a smartphone. We employed the Samsung Galaxy S4 and iPhone 4s smartphones to acquire tracheal sounds from N = 9 healthy volunteers at airflows ranging from 0.5 to 2.5 L/s. We found that the amplitude of the smartphone-acquired sounds was highly correlated with the airflow from a spirometer, and similar to previously-published studies, we found that the increasing tracheal sounds’ amplitude as flow increases follows a power law relationship. Acquired tracheal sounds were used for breath-phase onset detection and their onsets differed by only 52 ± 51 ms (mean ± SD) for Galaxy S4, and 51 ± 48 ms for iPhone 4s, when compared to those detected from the reference signal via the spirometer. Moreover, it was found that accurate respiratory rates (RR) can be obtained from tracheal sounds. The correlation index, bias and limits of agreement were r2 = 0.9693, 0.11 (−1.41 to 1.63) breaths-per-minute (bpm) for Galaxy S4, and r2 = 0.9672, 0.097 (–1.38 to 1.57) bpm for iPhone 4s, when compared to RR estimated from spirometry. Both smartphone devices performed similarly, as no statistically-significant differences were found.http://www.mdpi.com/1424-8220/14/8/13830respiratory soundstracheal soundssmartphonerespiratory ratebreath-phaseentropytime-frequency representation |
| spellingShingle | Bersain A. Reyes Natasa Reljin Ki H. Chon Tracheal Sounds Acquisition Using Smartphones Sensors respiratory sounds tracheal sounds smartphone respiratory rate breath-phase entropy time-frequency representation |
| title | Tracheal Sounds Acquisition Using Smartphones |
| title_full | Tracheal Sounds Acquisition Using Smartphones |
| title_fullStr | Tracheal Sounds Acquisition Using Smartphones |
| title_full_unstemmed | Tracheal Sounds Acquisition Using Smartphones |
| title_short | Tracheal Sounds Acquisition Using Smartphones |
| title_sort | tracheal sounds acquisition using smartphones |
| topic | respiratory sounds tracheal sounds smartphone respiratory rate breath-phase entropy time-frequency representation |
| url | http://www.mdpi.com/1424-8220/14/8/13830 |
| work_keys_str_mv | AT bersainareyes trachealsoundsacquisitionusingsmartphones AT natasareljin trachealsoundsacquisitionusingsmartphones AT kihchon trachealsoundsacquisitionusingsmartphones |