Personalized Sleep Spindle Detection in Whole Night Polysomnography.

Scafa, Stefano; Fiorillo, Luigi; Lucchini, Marta; Roth, Corinne; Agostini, Valentina; Vancheri, Alberto; Faraci, Francesca D (2020). Personalized Sleep Spindle Detection in Whole Night Polysomnography. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020, pp. 1047-1050. IEEE 10.1109/EMBC44109.2020.9176136

Text 09176136.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (1MB)

The present study proposes a new personalized sleep spindle detection algorithm, suggesting the importance of an individualized approach. We identify an optimal set of features that characterize the spindle and exploit a support vector machine to distinguish between spindle and nonspindle patterns. The algorithm is assessed on the open source DREAMS database, that contains only selected part of the polysomnography, and on whole night polysomnography recordings from the SPASH database. We show that on the former database the personalization can boost sensitivity, from 84.2% to 89.8%, with a slight increase in specificity, from 97.6% to 98.1%. On a whole night polysomnography instead, the algorithm reaches a sensitivity of 98.6% and a specificity of 98.1%, thanks to the personalization approach. Future work will address the integration of the spindle detection algorithm within a sleep scoring automated procedure.

Interest & Impact

Downloads

3 since deposited on 16 Nov 2020
2 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item