Sleep staging with movement-related signals |
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| Institution: | 1. CNR-IMM Catania Headquarters, VIII Strada, 5, 95121, Italy;2. SSC, Scuola Superiore di Catania, Via Valdisavoia, 9, 95123 Catania, Italy;3. Anton Paar TriTec Sa, Rue de la Gare 4 Galileo Center, 2034 Peseux, Switzerland;4. Electric, Electronics and Computer Engineering Department, University of Catania, V.le A. Doria, 6, 95125 Catania, Italy;5. Physikalisches Institut (IA), RWTH Aachen University, Sommerfeldstraße 14, 52074 Aachen, Germany;6. Department of Chemical Science, University of Catania, V.le A. Doria 6, 95125 Catania, Italy;1. School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China;2. State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;1. Research Center for Nanophotonic and Nanoelectronic Materials, School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu Province, China;2. State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China |
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| Abstract: | Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification. |
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