Detection of Sleep Apnea Using Sonar Smartphone Technology.

Clicks: 329
ID: 93744
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #48 of 127 articles by views in conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 127 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This paper assesses the performance of a new noncontact sensing system based on Sonar technology as a Sleep Disordered Breathing (SDB) screener. The respiration and movements of a subject in bed can be measured via a smartphone placed onto a bedside table equipped with a custom app. The app employs novel proprietary algorithms to identify sleep stages and detect SDB patterns.The SDB screener was trained on a set of 94 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous monitoring via a full polysomnography (PSG) system and a smartphone equipped with the app. An additional fully independent set of 68 recordings, uniformly distributed across SDB severity classes, were held out for independent testing. The performance on the test set is excellent and comparable to other existing ambulatory SDB screeners, with a sensitivity of 94% and specificity of 97%, for a clinical threshold for the Apnea Hypopnea Index (AHI) of 15 events/hour.The technology can easily be adopted to scale, as no purchase of dedicated sensors is needed, providing a much needed low- cost alternative for monitoring and potentially screening of large population segments. Furthermore, the non-invasive, contactless sensing does not interfere with the sleeping habits of the user, facilitating longitudinal assessment. This, in combination with the simultaneous measurement of the user's sleep quality, could provide invaluable insights in the subject's response to SDB therapy and lead to increased patient adherence.
Reference Key
lyon2019detectionconference Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lyon, Graeme;Tiron, Roxana;Zaffaroni, Alberto;Osman, Ahmed;Kilroy, Hannah;Lederer, Katharina;Fietze, Ingo;Penzel, Thomas;
Journal conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference
Year 2019
DOI
10.1109/EMBC.2019.8857836
URL
Keywords
bluetooth low energy (ble) lora ble long range ieee 802.11ah isa100.11a industrial internet of things (iiot) narrowband iot (nb-iot) time slotted channel hopping (tsch) wifi halow wirelesshart

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.