Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/13789
Title: A Wearable Device Integrated with Deep Learning-Based Algorithms for the Analysis of Breath Patterns
Authors: Tarım, Ergün Alperay
Erimez, Büşra
Değirmenci, Mehmet
Tekin, H. Cumhur
Keywords: breath analyses
deep learning
object detection
sleep apnea
wearable devices
OBSTRUCTIVE SLEEP-APNEA
SENSOR
PRESSURE
SYSTEM
Publisher: Wiley
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.
Description: Article; Early Access
URI: https://doi.org/10.1002/aisy.202300174
https://hdl.handle.net/11147/13789
ISSN: 2640-4567
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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