Challenges in calculating the AHI to diagnose sleep apnoea using deep learning and portable monitors
- Automatic detection of the apnoea–hypopnoea index (AHI) using a portable monitor (PM) with artificial intelligence (AI) represents a significant challenge. The objective of this study was to examine factors that affect the performance of an AI algorithm that had been previously trained in calculating the AHI with polysomnography (PSG) data using signals collected by a PM.
| Author: | Ángel Serrano Alarcón, Natividad Martínez Madrid, Ralf SeepoldORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1007/s11818-025-00508-4 |
| ISSN: | 1439-054X |
| ISSN: | 1432-9123 |
| Parent Title (English): | Somnologie |
| Publisher: | Springer |
| Place of publication: | Berlin; Heidelberg |
| Document Type: | Article |
| Language: | English |
| Year of Publication: | 2025 |
| Release Date: | 2025/05/30 |
| Tag: | Sleep apnea syndromes; Artificial intelligence; Polysomnography; Algorithms; Obstructive leep apnea |
| First Page: | 1 |
| Last Page: | 5 |
| Institutes: | Institut für Angewandte Forschung - IAF |
| Open Access?: | Ja |
| Relevance: | Wiss. Zeitschriftenartikel reviewed: Listung in Positivlisten |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
