Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component
This study’s primary objective was to identify individuals whose physiological responses deviated from the rest of the study population by automatically monitoring atmospheric pressure levels to which they are exposed and using parameters derived from their heart rate variability (HRV). To achieve t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/2/447 |
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| author | María Dolores Peláez-Coca Alberto Hernando María Teresa Lozano Juan Bolea David Izquierdo Carlos Sánchez |
| author_facet | María Dolores Peláez-Coca Alberto Hernando María Teresa Lozano Juan Bolea David Izquierdo Carlos Sánchez |
| author_sort | María Dolores Peláez-Coca |
| collection | DOAJ |
| description | This study’s primary objective was to identify individuals whose physiological responses deviated from the rest of the study population by automatically monitoring atmospheric pressure levels to which they are exposed and using parameters derived from their heart rate variability (HRV). To achieve this, 28 volunteers were placed in a dry hyperbaric chamber, where they experienced varying pressures from 1 to 5 atmospheres, with five sequential stops lasting five minutes each at different atmospheric pressures. The HRV was dissected into two components: the respiratory component, which is linked to respiration; and the residual component, which is influenced by factors beyond respiration. Nine parameters were assessed, including the respiratory rate, four classic HRV temporal parameters, and four frequency parameters. A k-nearest neighbors classifier based on cosine distance successfully identified the atmospheric pressures to which the subjects were exposed to. The classifier achieved an 88.5% accuracy rate in distinguishing between the 5 atm and 3 atm stages using only four features: respiratory rate, heart rate, and two frequency parameters associated with the subjects’ sympathetic responses. Furthermore, the study identified 6 out of 28 subjects as having atypical responses across all pressure levels when compared to the majority. Interestingly, two of these subjects stood out in terms of gender and having less prior diving experience, but they still exhibited normal responses to immersion. This suggests the potential for establishing distinct safety protocols for divers based on their previous experience and gender. |
| first_indexed | 2024-03-08T09:47:25Z |
| format | Article |
| id | doaj.art-cc1c3e7b42774f35bc9dbb538b212465 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:47:25Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cc1c3e7b42774f35bc9dbb538b2124652024-01-29T14:14:51ZengMDPI AGSensors1424-82202024-01-0124244710.3390/s24020447Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory ComponentMaría Dolores Peláez-Coca0Alberto Hernando1María Teresa Lozano2Juan Bolea3David Izquierdo4Carlos Sánchez5Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, SpainBSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, SpainCentro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, SpainCentro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, SpainGTF Group, I3A Institute, University of Zaragoza, 50009 Zaragoza, SpainBSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, SpainThis study’s primary objective was to identify individuals whose physiological responses deviated from the rest of the study population by automatically monitoring atmospheric pressure levels to which they are exposed and using parameters derived from their heart rate variability (HRV). To achieve this, 28 volunteers were placed in a dry hyperbaric chamber, where they experienced varying pressures from 1 to 5 atmospheres, with five sequential stops lasting five minutes each at different atmospheric pressures. The HRV was dissected into two components: the respiratory component, which is linked to respiration; and the residual component, which is influenced by factors beyond respiration. Nine parameters were assessed, including the respiratory rate, four classic HRV temporal parameters, and four frequency parameters. A k-nearest neighbors classifier based on cosine distance successfully identified the atmospheric pressures to which the subjects were exposed to. The classifier achieved an 88.5% accuracy rate in distinguishing between the 5 atm and 3 atm stages using only four features: respiratory rate, heart rate, and two frequency parameters associated with the subjects’ sympathetic responses. Furthermore, the study identified 6 out of 28 subjects as having atypical responses across all pressure levels when compared to the majority. Interestingly, two of these subjects stood out in terms of gender and having less prior diving experience, but they still exhibited normal responses to immersion. This suggests the potential for establishing distinct safety protocols for divers based on their previous experience and gender.https://www.mdpi.com/1424-8220/24/2/447hyperbaric environmentsautonomic nervous systemheart rate variabilitysubject classificationorthogonal subspace projection |
| spellingShingle | María Dolores Peláez-Coca Alberto Hernando María Teresa Lozano Juan Bolea David Izquierdo Carlos Sánchez Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component Sensors hyperbaric environments autonomic nervous system heart rate variability subject classification orthogonal subspace projection |
| title | Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component |
| title_full | Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component |
| title_fullStr | Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component |
| title_full_unstemmed | Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component |
| title_short | Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component |
| title_sort | heart rate variability to automatically identify hyperbaric states considering respiratory component |
| topic | hyperbaric environments autonomic nervous system heart rate variability subject classification orthogonal subspace projection |
| url | https://www.mdpi.com/1424-8220/24/2/447 |
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