| Autor: |
McCloy K, Duce B, Hukins C, Abeyratne U |
| Jazyk: |
angličtina |
| Zdroj: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2021 Nov; Vol. 2021, pp. 704-707. |
| DOI: |
10.1109/EMBC46164.2021.9629998 |
| Abstrakt: |
Obstructive Sleep Apnea (OSA) is a sleep disorder associated with reduced vigilance. Vigilance status is often measured using the Psychomotor Vigilance Task (PVT). This paper investigates modelling strategies to map sleep spindle (Sp) characteristics to PVT metrics in patients with OSA. Sleep spindles (n=2305) were manually detected across blocks of sleep for 20 patients randomly selected from a cohort of 190 undergoing Polysomnography (PSG) for suspected OSA. Novel Sp metrics based on runs or "bursts" of Sps were used to model Sp characteristics to standardized (z) Lapse and Median Reaction Time (MdRT) scores, and to Groups based on zLapse and zMdRT scores. A model employing Sp Burst characteristics mapped to MdRT Group membership with an accuracy of 91.9%, (95% C.I. 90.8-93.0). The model had a sensitivity of 88.9%, (95% C.I. 87.5-89.0) and specificity of 89.1% (95% C.I. 87.3-90.5) for detecting patients with the lowest MdRTs in our cohort.Clinical Relevance- Based on these results it may be possible to use Sp data collected during overnight diagnostic PSG for OSA to detect patients at risk for attention deficits. This would improve triage for OSA therapy by identifying at risk patients at the time of OSA diagnosis and would remove the need to employ additional testing to assess vigilance status. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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