Key result
K-SVD learned dictionaries provided high reconstruction accuracy with short computational time for EEG compression with compressed sensing.
K-SVD learned dictionaries offer an efficient and accurate method for compressing clinical EEG signals, which is valuable for wearable health-care monitoring systems.
May support efficient wearable EEG monitoring; leaves open validation in cardiovascular applications.
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.
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Dao et al. (2018) studied Clinical EEG. K-SVD learned dictionaries vs. Static dictionaries (Gabor, Fourier, wavelets) was evaluated on Reconstruction accuracy and computational complexity. K-SVD learned dictionaries provided high reconstruction accuracy with short computational time for EEG compression with compressed sensing.
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