Novel Sleep Apnea Detection Based on UWB Artificial Intelligence Mattress

Chiapin Wang, Jen Hau Chan, Shih Hau Fang, Ho Ti Cheng, Yeh Liang Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In this paper, we propose a novel sleep apnea identification system by adopting a sleep breathing monitoring mattress which utilizes the ultra-wideband (UWB) physiological sensing technique. Unlike traditional methods which need wearable devices and electrical equipment connected to patients, the proposed system detects apnea in a non-conscious and non-contact way by using UWB sensors. The proposed system is built by a machine learning technique in the offline stage, and detects apnea in the online stage by using our designed apnea detection algorithm. The experimental results illustrate that the proposed apnea identification system efficiently detects sleep apnea without diagnoses undertaken at hospitals.

Original languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages158-159
Number of pages2
ISBN (Electronic)9781538678848
DOIs
Publication statusPublished - 2019 Mar
Event1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan
Duration: 2019 Mar 182019 Mar 20

Publication series

NameProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

Conference

Conference1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
Country/TerritoryTaiwan
CityHsinchu
Period2019/03/182019/03/20

Keywords

  • Sleep apnea detection
  • machine learning
  • ultra-wideband (UWB)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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