K-SVD Dictionary Learning Applied in Clinical EEG Compressed Sensing
| Autor: | Hung Ngoc Do, Anthony Griffin, Xue Jun Li, Phuong Thi Dao |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
K-SVD
medicine.diagnostic_test Computational complexity theory Computer science business.industry Wearable computer 020206 networking & telecommunications Context (language use) Pattern recognition Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Electroencephalography ComputingMethodologies_PATTERNRECOGNITION Compressed sensing Wavelet Compression (functional analysis) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 2018 International Conference on Advanced Technologies for Communications (ATC). |
| Popis: | Compression of electroencephalogram (EEG) signals has long been a challenging research topic. Recently, compressed sensing (CS) was proposed for EEG acquisition and compression with the rapid development of wearable health-care monitoring systems. Building an optimal dictionary to achieve excellent reconstruction accuracy and low computational complexity is extremely desirable in this context. While most existing work has focused on static dictionaries such as Gabor, Fourier and wavelets, the dynamic nature of EEG signals motivates us to study learned dictionaries. In this paper, we study how to build K-SVD learned dictionaries and then adopt them in EEG compression with CS. For the convenience of comparison, the well-established database of scalp EEG signals from Physiobank is used in the numerical experiments. Results demonstrate that a K-SVD learned dictionary provides high reconstruction accuracy with a short computational time. |
| Databáze: | OpenAIRE |
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