Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/13789
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tarım, Ergün Alperay | - |
dc.contributor.author | Erimez, Büşra | - |
dc.contributor.author | Değirmenci, Mehmet | - |
dc.contributor.author | Tekin, H. Cumhur | - |
dc.date.accessioned | 2023-10-03T07:15:34Z | - |
dc.date.available | 2023-10-03T07:15:34Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2640-4567 | - |
dc.identifier.uri | https://doi.org/10.1002/aisy.202300174 | - |
dc.identifier.uri | https://hdl.handle.net/11147/13789 | - |
dc.description | Article; Early Access | en_US |
dc.description.abstract | Sleep problems are serious issues that make life difficult for all people, including sleep apnea. Sleep apnea, which causes breathlessness for more than 10 s, is linked to severe health problems due to the serious damage it can induce. To mitigate the risk of these disorders, the monitoring of patients has become increasingly challenging. Wearable technologies offer an effective healthcare solution for remote patient monitoring and diagnosis. A novel wearable system based on Arduino technology is introduced, specifically designed to monitor the breath patterns of patients. The analysis of breath data from patients holds great importance for the diagnosis and continuous monitoring of sleep apnea. To address this need, an advanced image processing system based on deep learning techniques is presented. This system automatically detects respiratory patterns, including inhalation, exhalation, and breathlessness. The device has an average of 97.6% sensitivity, 79.7% specificity, and 96% accuracy in identifying breath patterns. The designed device can offer patients and healthcare institutions a simple, inexpensive, noninvasive, and ergonomic system for the analysis of breath patterns that can be further extended for sleep apnea diagnosis. | en_US |
dc.description.sponsorship | Turkish Academy of Science [TUBA GEBIP 2020]; Science Academy (Bilim Akademisi) [BAGEP 2022]; Izmir Institute of Technology (IZTECH) [2020IYTE0042]; Scientific and Technological Research Council of Turkey (TUBITAK); Turkish Council of Higher Education; TUBITAK | en_US |
dc.description.sponsorship | & nbsp;H.C.T. would like to thank the Outstanding Young Scientists Award funding (TUBA GEBIP 2020) from the Turkish Academy of Science, the Young Scientist Awards (BAGEP 2022) from the Science Academy (Bilim Akademisi), and the scientific research project (2020IYTE0042) funded by Izmir Institute of Technology (IZTECH). E.A.T. acknowledges the support of The Scientific and Technological Research Council of Turkey (TUBITAK) for the 2211-A BIDEB doctoral scholarship and the support of the Turkish Council of Higher Education for the 100/2000 CoHE doctoral scholarship. B.E. acknowledges the support of TUBITAK for the 2247-C STAR intern researcher scholarship. The authors would like to dedicate this article to the loving memories of our lost ones in the 2023 Kahramanmaras Earthquake. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Wiley | en_US |
dc.relation.ispartof | Advanced Intelligent Systems | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | breath analyses | en_US |
dc.subject | deep learning | en_US |
dc.subject | object detection | en_US |
dc.subject | sleep apnea | en_US |
dc.subject | wearable devices | en_US |
dc.subject | OBSTRUCTIVE SLEEP-APNEA | en_US |
dc.subject | SENSOR | en_US |
dc.subject | PRESSURE | en_US |
dc.subject | SYSTEM | en_US |
dc.title | A Wearable Device Integrated with Deep Learning-Based Algorithms for the Analysis of Breath Patterns | en_US |
dc.type | Article | en_US |
dc.institutionauthor | … | - |
dc.department | İzmir Institute of Technology | en_US |
dc.identifier.wos | WOS:001050244200001 | en_US |
dc.identifier.scopus | 2-s2.0-85168392744 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1002/aisy.202300174 | - |
dc.authorscopusid | 57200283702 | - |
dc.authorscopusid | 58541825500 | - |
dc.authorscopusid | 58018755000 | - |
dc.authorscopusid | 56781554300 | - |
dc.identifier.wosquality | N/A | - |
dc.identifier.scopusquality | Q4 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | No Fulltext | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
crisitem.author.dept | 03.01. Department of Bioengineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
1
checked on Nov 29, 2024
Page view(s)
122
checked on Dec 2, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.