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

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology

UniBE Contributor:

Roth Wälti, Corinne

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2694-0604

Publisher:

IEEE

Language:

English

Submitter:

Chantal Kottler

Date Deposited:

16 Nov 2020 17:09

Last Modified:

16 Nov 2020 21:20

Publisher DOI:

10.1109/EMBC44109.2020.9176136

PubMed ID:

33018165

BORIS DOI:

10.7892/boris.147424

URI:

https://boris.unibe.ch/id/eprint/147424

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