SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals
Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini · 2024
Read SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals by Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11645058.txt.
Description
Davide Lillini and co-authors present a system to spot sleep apnea from a throat microphone in a 2024 paper. Sleep apnea, where breathing pauses in sleep, affects a share of the world population and often goes unfound. The standard test is intrusive and relies on expert judgment, so a simpler aid could widen screening.
Their system turns sound into a frequency image and processes it with a paired network design that mixes image and sequence layers. An unsupervised grouping step then marks apnea events. Tests on overnight records from eight patients reached a high recall for apnea events. The authors compare their system with a recent fully image based method and discuss the best input settings.
Catalog record
| CREATOR | Davide Lillini, Carlo Aironi, Lucia Migliorelli, Leonardo Gabrielli, Stefano Squartini |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |