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.
Personalized Sleep Spindle Detection in Whole Night Polysomnography / Scafa, S.; Fiorillo, L.; Lucchini, M.; Roth, C.; Agostini, V.; Vancheri, A.; Faraci, F. D.. - ELETTRONICO. - 2020-:(2020), pp. 1047-1050. (Intervento presentato al convegno 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 tenutosi a Montreal (Canada) nel 2020) [10.1109/EMBC44109.2020.9176136].
Personalized Sleep Spindle Detection in Whole Night Polysomnography
Agostini V.;
2020
Abstract
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2846589