Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 40, Issue 17, Pages 7046-7059Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2013.06.023
Keywords
Automatic sleep staging; The maximum overlap discrete wavelet transform; Polysomnographic signals; Features selection; Sleep dataset
Categories
Funding
- Portuguese Foundation for Science and Technology (FCT) [SFRH/BD/81828/2011, SFRH/BD/80735/2011]
- FCT [AMS-HMI12: RECI/EEI-AUT/0181/2012]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/80735/2011, SFRH/BD/81828/2011] Funding Source: FCT
Ask authors/readers for more resources
To improve applicability of automatic sleep staging an efficient subject-independent method is proposed with application in sleep-wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep). In turn, NREM is further divided into three stages denoted here by N1, N2, and N3. To assess the method, polysomnographic (PSG) records of 40 patients from our ISRUC-Sleep dataset, which was scored by an expert clinician in the central hospital of Coimbra, are used. To find the best combination of PSG signals for automatic sleep staging, six electroencephalographic (EEG), two electrooculographic (EOG), and one electromyographic (EMG) channels are analyzed. An extensive set of feature extraction techniques are applied, covering temporal, frequency and time-frequency domains. The maximum overlap wavelet transform (MODWT), a shift invariant transform, was used to extract the features in time-frequency domain. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. The most discriminative features are selected through a two-step method composed by a manual selection step based on features' histogram analysis followed by an automatic feature selector. The selected feature set is classified using support vector machines (SVMs). The system achieved the best performance by combining 6 channels (C3, C4, O1, left EOG (LOC), right EOG (ROC) and chin EMG (X1)) for sleep-wake detection, and 9 channels (C3, C4, O1, O2, F3, F4, LOC, ROC, X1) for multiclass sleep staging. (C) 2013 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available