Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study
Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea–hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnos...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/23/9512 |
| _version_ | 1797399564430344192 |
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| author | Miguel A. Espinosa Pedro Ponce Arturo Molina Vicente Borja Martha G. Torres Mario Rojas |
| author_facet | Miguel A. Espinosa Pedro Ponce Arturo Molina Vicente Borja Martha G. Torres Mario Rojas |
| author_sort | Miguel A. Espinosa |
| collection | DOAJ |
| description | Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea–hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%. |
| first_indexed | 2024-03-09T01:42:02Z |
| format | Article |
| id | doaj.art-600c9253a72e40c580c00642d9402244 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:42:02Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-600c9253a72e40c580c00642d94022442023-12-08T15:26:15ZengMDPI AGSensors1424-82202023-11-012323951210.3390/s23239512Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive StudyMiguel A. Espinosa0Pedro Ponce1Arturo Molina2Vicente Borja3Martha G. Torres4Mario Rojas5Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, MexicoInstitute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, MexicoInstitute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, MexicoFaculty of Engineering, Universidad Nacional Autonoma de Mexico, Mexico City 04510, MexicoSleep Medicine Unit, Instituto Nacional de Enfermedades Respiratorias Ismael Cosio Villegas, Mexico City 14080, MexicoInstitute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Mexico City 14380, MexicoObstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea–hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.https://www.mdpi.com/1424-8220/23/23/9512sleep apnea detectionoximetryactigraphyrespiratory effortrespiratory flow |
| spellingShingle | Miguel A. Espinosa Pedro Ponce Arturo Molina Vicente Borja Martha G. Torres Mario Rojas Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study Sensors sleep apnea detection oximetry actigraphy respiratory effort respiratory flow |
| title | Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study |
| title_full | Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study |
| title_fullStr | Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study |
| title_full_unstemmed | Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study |
| title_short | Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study |
| title_sort | advancements in home based devices for detecting obstructive sleep apnea a comprehensive study |
| topic | sleep apnea detection oximetry actigraphy respiratory effort respiratory flow |
| url | https://www.mdpi.com/1424-8220/23/23/9512 |
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