Abstract : Sleep spindles are the most interesting hallmark of stage 2 sleep EEG. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload. In this paper two different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform and Automatic Visual Scoring. The results obtained using both methods are compared with human expert scorers.
Luis M. Camarinha-Matos; Ehsan Shahamatnia; Gonçalo Nunes. 3rd Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Feb 2012, Costa de Caparica, Portugal. Springer, IFIP Advances in Information and Communication Technology, AICT-372, pp.267-272, 2012, Technological Innovation for Value Creation. 〈10.1007/978-3-642-28255-3_29〉
https://hal.inria.fr/hal-01365594
Contributeur : Hal Ifip
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Dernière modification le : mardi 13 septembre 2016 - 17:06:02
Document(s) archivé(s) le : mercredi 14 décembre 2016 - 14:29:14
João Costa, Manuel Ortigueira, Arnaldo Batista. Short Time Fourier Transform and Automatic Visual Scoring for the Detection of Sleep Spindles. Luis M. Camarinha-Matos; Ehsan Shahamatnia; Gonçalo Nunes. 3rd Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Feb 2012, Costa de Caparica, Portugal. Springer, IFIP Advances in Information and Communication Technology, AICT-372, pp.267-272, 2012, Technological Innovation for Value Creation. 〈10.1007/978-3-642-28255-3_29〉. 〈hal-01365594〉
