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Sleep-spindle identification on EEG signals from polysomnographie recordings using correntropy | IEEE Conference Publication | IEEE Xplore

Sleep-spindle identification on EEG signals from polysomnographie recordings using correntropy


Abstract:

Sleep spindles (SSs) are characteristic electroencephalographic (EEG) waveforms of sleep stages N2 and N3. One of the main problems associated with SS detection is the hi...Show More

Abstract:

Sleep spindles (SSs) are characteristic electroencephalographic (EEG) waveforms of sleep stages N2 and N3. One of the main problems associated with SS detection is the high number of false positives. In this paper we propose a new periodogram based on correntropy to detect SSs and enhance their characterization. Correntropy is a generalized correlation, under the information theoretic learning framework. A non-negative matrix factorization decomposition of correntropy allows us to obtain a new periodogram, which shows an improved resolution capability compared to the conventional power spectrum density. Preliminary results show that the proposed method obtained a sensitivity rate of 0.868 with a false positive rate of 0.121.
Date of Conference: 16-20 August 2016
Date Added to IEEE Xplore: 18 October 2016
ISBN Information:

ISSN Information:

PubMed ID: 28269102
Conference Location: Orlando, FL, USA

References

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