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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Baran Pouyan, M. Nourani, M. Pompeo, M. |
| Copyright Year | 2015 |
| Description | Author affiliation: Quality of Life Technol. Lab., Univ. of Texas at Dallas, Richardson, TX, USA (Baran Pouyan, M.; Nourani, M.) || Presbyterian Wound Care Clinic, Dallas, TX, USA (Pompeo, M.) |
| Abstract | Sleep state detection is valuable in assessing patient's sleep quality and in-bed general behavior. In this paper, a novel classification approach of sleep states (sleep, pre-wake, wake) is proposed that uses only surface pressure sensors. In our method, a mobility metric is defined based on successive pressure body maps. Then, suitable statistical features are computed based on the mobility metric. Finally, a customized random forest classifier is employed to identify various classes including a new class for pre-wake state. Our algorithm achieves 96.1% and 88% accuracies for two (sleep, wake) and three (sleep, pre-wake, wake) class identification, respectively. |
| Starting Page | 1207 |
| Ending Page | 1210 |
| File Size | 933735 |
| Page Count | 4 |
| File Format | |
| ISSN | 1557170X |
| e-ISBN | 9781424492718 |
| DOI | 10.1109/EMBC.2015.7318583 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-25 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Sleep apnea Accuracy Sensors Electrocardiography Measurement |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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