Derivation of Respiratory Metrics in Health and Asthma
The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorith...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/24/7134 |
| _version_ | 1797544895390416896 |
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| author | Joseph Prinable Peter Jones David Boland Alistair McEwan Cindy Thamrin |
| author_facet | Joseph Prinable Peter Jones David Boland Alistair McEwan Cindy Thamrin |
| author_sort | Joseph Prinable |
| collection | DOAJ |
| description | The ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all <i>p</i> < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates. |
| first_indexed | 2024-03-10T14:07:02Z |
| format | Article |
| id | doaj.art-960754899ec44f9b8a4d28584801811c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:07:02Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-960754899ec44f9b8a4d28584801811c2023-11-21T00:32:50ZengMDPI AGSensors1424-82202020-12-012024713410.3390/s20247134Derivation of Respiratory Metrics in Health and AsthmaJoseph Prinable0Peter Jones1David Boland2Alistair McEwan3Cindy Thamrin4The School of Biomedical Engineering, University of Sydney, Darlington 2006, AustraliaThe School of Electrical and Information Engineering, University of Sydney, Darlington 2006, AustraliaThe School of Electrical and Information Engineering, University of Sydney, Darlington 2006, AustraliaThe School of Biomedical Engineering, University of Sydney, Darlington 2006, AustraliaThe Woolcock Institute of Medical Research, University of Sydney, Glebe 2037, AustraliaThe ability to continuously monitor breathing metrics may have indications for general health as well as respiratory conditions such as asthma. However, few studies have focused on breathing due to a lack of available wearable technologies. To examine the performance of two machine learning algorithms in extracting breathing metrics from a finger-based pulse oximeter, which is amenable to long-term monitoring. Methods: Pulse oximetry data were collected from 11 healthy and 11 with asthma subjects who breathed at a range of controlled respiratory rates. U-shaped network (U-Net) and Long Short-Term Memory (LSTM) algorithms were applied to the data, and results compared against breathing metrics derived from respiratory inductance plethysmography measured simultaneously as a reference. Results: The LSTM vs. U-Net model provided breathing metrics which were strongly correlated with those from the reference signal (all <i>p</i> < 0.001, except for inspiratory: expiratory ratio). The following absolute mean bias (95% confidence interval) values were observed (in seconds): inspiration time 0.01(−2.31, 2.34) vs. −0.02(−2.19, 2.16), expiration time −0.19(−2.35, 1.98) vs. −0.24(−2.36, 1.89), and inter-breath intervals −0.19(−2.73, 2.35) vs. −0.25(2.76, 2.26). The inspiratory:expiratory ratios were −0.14(−1.43, 1.16) vs. −0.14(−1.42, 1.13). Respiratory rate (breaths per minute) values were 0.22(−2.51, 2.96) vs. 0.29(−2.54, 3.11). While percentage bias was low, the 95% limits of agreement was high (~35% for respiratory rate). Conclusion: Both machine learning models show strong correlation and good comparability with reference, with low bias though wide variability for deriving breathing metrics in asthma and health cohorts. Future efforts should focus on improvement of performance of these models, e.g., by increasing the size of the training dataset at the lower breathing rates.https://www.mdpi.com/1424-8220/20/24/7134asthmarespiratory monitoringmachine learningU-NetLSTM |
| spellingShingle | Joseph Prinable Peter Jones David Boland Alistair McEwan Cindy Thamrin Derivation of Respiratory Metrics in Health and Asthma Sensors asthma respiratory monitoring machine learning U-Net LSTM |
| title | Derivation of Respiratory Metrics in Health and Asthma |
| title_full | Derivation of Respiratory Metrics in Health and Asthma |
| title_fullStr | Derivation of Respiratory Metrics in Health and Asthma |
| title_full_unstemmed | Derivation of Respiratory Metrics in Health and Asthma |
| title_short | Derivation of Respiratory Metrics in Health and Asthma |
| title_sort | derivation of respiratory metrics in health and asthma |
| topic | asthma respiratory monitoring machine learning U-Net LSTM |
| url | https://www.mdpi.com/1424-8220/20/24/7134 |
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