Name
Abstract | ||
|---|---|---|
Sleep spindles are important short-lasting waveforms in the sleep EEC They are the hallmarks of the so-called Stage 2 sleep. Automated methods for spindle detection presented in literature typically use some form of fixed spindle amplitude threshold. The problem with that approach is that it is poor against inter-subject variability in spindle amplitudes. In this work a spindle detection method without an amplitude threshold was considered. Two versions of the method were compared as fuzzy reasoning and an Autoassociative Multilayer Perceptron (A-MLP) network were both employed for the classification between sleep spindles and non-spindle EEG activities. A novel training procedure was developed to remove inconsistencies from the training data of the A-MLP. This improvement of training data was found to have a positive effect on the method performance on the test data. However, in this comparison the fuzzy reasoning produced a better spindle detection result, probably due to the small size of the A-MLP. |
Year | Venue | Keywords |
|---|---|---|
2000 | EUSIPCO | feature extraction,sleep,training data,visualization,electroencephalography |
Field | DocType | ISBN |
Training set,Sleep spindle,Fuzzy reasoning,Visualization,Computer science,Feature extraction,Speech recognition,Multilayer perceptron,Test data,Electroencephalography | Conference | 978-952-1504-43-3 |
Citations | PageRank | References |
1 | 0.58 | 1 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| E Huupponen | 1 | 46 | 7.20 |
| alpo varri | 2 | 1 | 0.58 |
| Joel Hasan | 3 | 23 | 3.05 |
| sanleena himanen | 4 | 1 | 0.58 |
| M Lehtokangas | 5 | 158 | 21.87 |
| Jukka Saarinen | 6 | 264 | 46.21 |