TY - JOUR
T1 - Automatic SLEEP staging
T2 - From young aduslts to elderly patients using multi-class support vector machine
AU - Kempfner, Jacob
AU - Jennum, Poul
AU - Sorensen, Helge B D
AU - Christensen, Gitte Julie
AU - Nikolic, Miki
PY - 2013
Y1 - 2013
N2 - Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0.91. Validation of the sleep stage detector in other sleep disorders, such as apnea and narcolepsy, should be considered in future work.
AB - Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0.91. Validation of the sleep stage detector in other sleep disorders, such as apnea and narcolepsy, should be considered in future work.
U2 - 10.1109/embc.2013.6610864
DO - 10.1109/embc.2013.6610864
M3 - Journal article
C2 - 24111051
SN - 0589-1019
VL - 2013
SP - 5777
EP - 5780
JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ER -