Name
Abstract | ||
|---|---|---|
Human sleep is divided into two segments, Rapid Eye Movement (REM) sleep and Non-REM (NREM) sleep. NREM sleep is further divided into 4 stages. Sleep staging attempts to identify these stages based on the signals collected in PSG. Significant information can be derived from the EEG signals collected during PSG. Wavelet coefficients are extracted from EEG signals. In order to reduce the amount of data set, the statistical features are calculated from wavelet coefficients. For performing decision making, six ANFIS classifiers and SVM classifier are used to differentiate between REM and Non-REM sleep stages. That is to say, pattern varies under the different sleep stages. Therefore, healthy humans with a regular night's sleep will follow these sleep stages in a particular pattern. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596732 | 2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 |
Keywords | Field | DocType |
svm,sleep,support vector machines,non rem sleep,time frequency analysis,anfis,neurophysiology,wavelet transforms,electroencephalography,eeg | Polysomnogram,Pattern recognition,Computer science,Support vector machine,Non-rapid eye movement sleep,Speech recognition,Eye movement,Artificial intelligence,Sleep Stages,Electroencephalography,Wavelet transform,Wavelet | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maryam Vatankhah | 1 | 10 | 1.49 |
| Mohammad R. Akbarzadeh-Totonchi | 2 | 125 | 18.26 |
| A. Moghimi | 3 | 0 | 0.34 |