Apnoea-Pi: Sleep disorder monitoring with open-source electronics and acoustics

Jethro Vernon, Pep Canyelles-Pericas, Hamdi Torun, Richard Binns, Wai Pang Ng, Yongqing (Richard) Fu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)
36 Downloads (Pure)

Abstract

Apnoea is a sleep disorder that affects an increasing number of adults causing harm from fatigue to a growing chance of heart problems. Apnoea disorders can be treated but advanced monitoring and diagnosing tools are needed to identify its strand and offer adequate treatment. Therefore, Apnoea tracking is vital to help keep patients healthy. Sleep Apnoea can cause a number of conditions such as fatigue, high blood pressure, liver functionality and an increased risk of type 2 diabetes. These complications make it necessary to monitor as many potential patients as possible by designing an instrument that is accurate, comfortable to use, fit for purpose, cost effective and with embedded computation capabilities to store, process and transmit time series data. In this work we present Apnoea-Pi, an adaptation of our Acousto-Pi open source surface acoustic wave platform to monitor Apnoea in patients using ultrasonic humidity sensing.

Original languageEnglish
Title of host publication2021 26th International Conference on Automation and Computing
Subtitle of host publicationSystem Intelligence through Automation and Computing, ICAC 2021
EditorsChenguang Yang
Place of PublicationPiscataway
PublisherIEEE
ISBN (Electronic)9781860435577
ISBN (Print)9781665443524
DOIs
Publication statusPublished - 2 Sept 2021
Event26th International Conference on Automation and Computing, ICAC 2021 - Portsmouth, United Kingdom
Duration: 2 Sept 20214 Sept 2021

Conference

Conference26th International Conference on Automation and Computing, ICAC 2021
Country/TerritoryUnited Kingdom
CityPortsmouth
Period2/09/214/09/21

Keywords

  • apnoea
  • open-source electronics
  • pattern recognition
  • piezoelectric thin film
  • sleep disorder
  • Surface acoustic waves
  • time series identification

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