使用SPIHT对多通道脑电图进行预处理以提高压缩性能。
Pre-processing of multi-channel EEG for improved compression performance using SPIHT.
作者信息
Daou Hoda, Labeau Fabrice
机构信息
Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2232-5. doi: 10.1109/EMBC.2012.6346406.
A novel technique for Electroencephalogram (EEG) compression is proposed in this article. This technique makes use of the inter-channel redundancy present between different EEG channels of the same recording and the intra-channel redundancy between the different samples of a specific channel. It uses Discrete Wavelet Transform (DWT) and Set partitioning in hierarchical trees (SPIHT) in 2-D to code the EEG channels. Smoothness transforms are added in order to guarantee good performance of SPIHT in 2-D. Experimental results show that this technique is able to provide low distortion values for high compression ratios (CRs). In addition, performance results of this method do not vary a lot between different patients which proves the stability of the method when used with recordings of different characteristics.
本文提出了一种用于脑电图（EEG）压缩的新技术。该技术利用同一记录中不同EEG通道之间存在的通道间冗余以及特定通道不同样本之间的通道内冗余。它使用二维离散小波变换（DWT）和分层树中的集合划分（SPIHT）对EEG通道进行编码。添加了平滑变换以保证SPIHT在二维中的良好性能。实验结果表明，该技术能够在高压缩率（CR）下提供低失真值。此外，该方法在不同患者之间的性能结果差异不大，这证明了该方法在用于不同特征记录时的稳定性。
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