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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dissanayaka, C. Cvetkovic, D. Patti, C.R. Shahrbabaki, S.S. Ahmed, B. Schilling, C. Schredl, M. |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Electr. & Comput. Eng., RMIT Univ., Melbourne, VIC, Australia (Dissanayaka, C.; Cvetkovic, D.; Patti, C.R.; Shahrbabaki, S.S.) || Texas A&M Univ., Doha, Qatar (Ahmed, B.) || Sleep Lab., Central Inst. of Mental Health, Mannheim, Germany (Schilling, C.; Schredl, M.) |
| Abstract | In the past several studies have evaluated the human sleep onset (wake to sleep transition) using the electroencephalographic (EEG) measurements. This paper has evaluated the detection accuracy of sleep stages for multiple features based on the EEG alpha activity, during SO in healthy, insomniac and schizophrenic patients. The features include topographic features such as Directed Transfer Function, Full frequency DTF, Welch Coherence, Minimum Variance Distortionless Response Coherence and Partial Directed Coherence. Sleep stages Wake, NREM (Non-rapid Eye Movement) stages 1 and 2 were classified using Artificial Neural Networks (ANN) classifier and evaluated using classification accuracy. The results suggest that using topographic set of features yield an agreement of 81.3 % with the whole database classification of human expert. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 681112 |
| Page Count | 4 |
| File Format | |
| e-ISBN | 9781479972340 |
| DOI | 10.1109/BioCAS.2015.7348362 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-22 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Sleep Coherence Artificial neural networks Brain modeling Electroencephalography Mathematical model Standards |
| Content Type | Text |
| Resource Type | Article |
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