| Autor: |
Arindam Gajendra Mahapatra, Balbir Singh, Hiroaki Wagatsuma, Keiichi Horio |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Advances in Signal Processing, Vol 2018, Iss 1, Pp 1-12 (2018) |
| Druh dokumentu: |
article |
| ISSN: |
1687-6180 |
| DOI: |
10.1186/s13634-018-0568-2 |
| Popis: |
Abstract In this paper, we have proposed an application of sparse-based morphological component analysis (MCA) to address the problem of classification of the epileptic seizure using time series electroencephalogram (EEG). MCA was employed to decompose the EEG signal segments considering its morphology during epileptic events using undecimated wavelet transform (UDWT), local discrete cosine transform (LDCT), and Dirac bases forming the over-complete dictionary. Frequency-modulated time frequency features were extracted after applying the Hilbert transform. Feature root mean instantaneous frequency square (RMIFS) and its parameters and parameters ratio are used in two different pairs for classification using support vector machine (SVM), showing good and comparable results. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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