Sleep Apnea Detection System using Internet of Things and Deep Learning Techniques

DOI

10.2991/978-94-6463-754-0_9How to use a DOI?

Keywords

Apnea; Sensor; LSTM; AHI

Abstract

Sleep disorder is a common sleep disorder marked by recurrent disruptions in respiration during sleep. Prompt identification and surveillance of sleep disorder are essential for accurate diagnosis and intervention. We propose an innovative method for detecting sleep disorder utilizing Ultra-Wideband (UWB) sensor technology. The UWB sensor technology provides numerous benefits, such as exceptional accuracy, little power usage, and the capability to traverse various materials. We intend to utilize these properties to create a non-intrusive and economical approach for the precise detection of sleep disorder occurrences. Our study centers on the development of a wearable Wide Band sensorbased device which can be conveniently positioned in proximity to the subjects sleeping region. Wide Band sensors are used to assess and interpret minor motions and Physical information during sleep also. We utilize an innovative signal processing technique to detect sleep disorder occurrences. By extracting particular elements from the Wide Band sensor data, we may discern the distinctive patterns linked to sleep disorder changes. These signals are fed to a Deep Learning model called LSTM where data will be processed to identify variations in the sensor data to detect normal and abnormal sleep. The suggested system may offer an efficient solution for the initial detection and ongoing identification of sleep disorder, resulting in enhanced diagnosis, treatment, and overall patient care. Additional investigation into developing a website for hospitals to track disorder patients. Mobile application developing for health care.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - K. V. Jisha
AU  - T. Jayanthi
PY  - 2025
DA  - 2025/06/30
TI  - Sleep Apnea Detection System using Internet of Things and Deep Learning Techniques
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 81
EP  - 92
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_9
DO  - 10.2991/978-94-6463-754-0_9
ID  - Jisha2025
ER  -