Sleep stage classification using sparse rational decomposition of single channel EEG records
المؤلف | Samiee K. |
المؤلف | Kovacs P. |
المؤلف | Kiranyaz, Mustafa Serkan |
المؤلف | Gabbouj M. |
المؤلف | Saramaki T. |
تاريخ الإتاحة | 2022-04-26T12:31:23Z |
تاريخ النشر | 2015 |
اسم المنشور | 2015 23rd European Signal Processing Conference, EUSIPCO 2015 |
المصدر | Scopus |
المعرّف | http://dx.doi.org/10.1109/EUSIPCO.2015.7362706 |
الملخص | A sparse representation of ID signals is proposed based on time-frequency analysis using Generalized Rational Discrete Short Time Fourier Transform (RDSTFT). First, the signal is decomposed into a set of frequency sub-bands using poles and coefficients of the RDSTFT spectra. Then, the sparsity is obtained by applying the Basis Pursuit (BP) algorithm on these frequency sub-bands. Finally, the total energy of each subband was used to extract features for offline patient-specific sleep stage classification of single channel EEG records. In classification of over 670 hours sleep Electroencephalography of 39 subjects, the overall accuracy of 92.50% on the test set is achieved using random forests (RF) classifier trained on 25% of each sleep record. A comparison with the results of other state-of-art methods demonstrates the effectiveness of the proposed sparse decomposition method in EEG signal analysis. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Biomedical signal processing Classification (of information) Decision trees Electroencephalography Electrophysiology Rational functions Sleep research basis pursuit Discrete short time fourier transforms Sleep stage sleep-EDF Sparse decomposition Sparse representation sparsity Time frequency analysis Signal processing |
النوع | Conference Paper |
الصفحات | 1860-1864 |
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