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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/FE586237-689D-4D4F-8B19-516C33D5A023 | - |
| Identifier | http://www.researchgate.net/profile/Vangelis_Sakkalis/publication/224163305_Comparative_analysis_of_time-frequency_methods_estimating_the_time-varying_microstructure_of_sleep_EEG_spindles/links/0fcfd50c74d6cb7439000000.pdf | - |
| Language | en | - |
| Extent | 5 pages | en |
| Title | Comparative analysis of time-frequency methods estimating the time-varying microstructure of sleep EEG spindles | en |
| Creator | Zervakis Michalis | en |
| Creator | Ζερβακης Μιχαλης | el |
| Creator | Paparrigopoulos, T | en |
| Creator | H. Tsekou | en |
| Creator | Sakkalis, Vangelis | en |
| Creator | Golemati, S | en |
| Content Summary | Parameter estimation for an assumed sleep EEG spindle model (AM-FM signal) is performed by using four time-frequency analysis methods. Results from simulated as well as from real data are presented. In simulated data, the Hilbert Transform-based method has the lowest average percentage error but produces considerable signal distortion. The Complex Demodulation and the Matching Pursuit-based methods have error rates below 10%, but the Matching Pursuit-based method produces considerable signal distortion as well. The Wavelet Transform-based method has the poorest performance. In real data, all methods produce reasonable parameter values. However, the Hilbert Transform and the Matching Pursuit- based methods may not be applicable for sleep spindles shorter than about 0.8 sec. Matching Pursuit-based curve fitting is utilized as part of the parameter estimation process. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-10-23 | - |
| Date of Publication | 2006 | - |
| Subject | Biomedicine | en |
| Bibliographic Citation | en |
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