Key result
The proposed 1.5-D multi-channel EEG compression algorithm outperforms the 2-D NLSPIHT algorithm under the same compression ratio and provides better fidelity at higher compression ratios.
The proposed 1.5-D EEG compression algorithm offers lower computational complexity and better fidelity at high compression ratios compared to existing 2-D methods.
May enable lower-power multi-channel EEG devices; leaves open clinical validation against 2-D methods.
This letter proposes a novel 1.5-D algorithm for multi-channel electroencephalogram (EEG) compression. The proposed algorithm only needs to perform 1-D Discrete Wavelet Transform (DWT) rather than the 2-D version employed by previous works, and thus it results in lower computational complexity and power dissipation. In this algorithm, a new 2-D arranging method that exploits correlations between different sub-bands is developed to concentrate the energy, which causes more efficient compression using No List Set Partitioning in Hierarchical Trees (NLSPIHT) algorithm. Experimental results demonstrate that the proposed algorithm outperforms 2-D NLSPIHT algorithm under the same compression ratio (CR) and it is slightly inferior to 2-D SPIHT algorithm in the near-lossless compression regime, but it can provide a better fidelity with respect to higher CRs.
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Xu et al. (2015) studied this question. 1.5-D multi-channel EEG compression algorithm based on NLSPIHT vs. 2-D NLSPIHT and 2-D SPIHT algorithms was evaluated on Compression ratio and fidelity. The proposed 1.5-D multi-channel EEG compression algorithm outperforms the 2-D NLSPIHT algorithm under the same compression ratio and provides better fidelity at higher compression ratios.
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