From young aduslts to elderly patients using multi-class support vector machine
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.
2013 35th Annual International Conference of the Ieee Engineering in Medicine and Biology Society, 2013, Vol 2013, p. 5777-5780
diseases; electro-oculography; electroencephalography; geriatrics; medical disorders; medical signal detection; signal classification; sleep; support vector machines; Engineered Materials, Dielectrics and Plasmas; Journal Article
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2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013