Capurro Ignacio, Lecumberry Federico, Martin Alvaro, Ramirez Ignacio, Rovira Eugenio, Seroussi Gadiel
IEEE J Biomed Health Inform. 2017 Jul;21(4):904-916. doi: 10.1109/JBHI.2016.2582683. Epub 2016 Jun 21.
This paper proposes lossless and near-lossless compression algorithms for multichannel biomedical signals. The algorithms are sequential and efficient, which makes them suitable for low-latency and low-power signal transmission applications. We make use of information theory and signal processing tools (such as universal coding, universal prediction, and fast online implementations of multivariate recursive least squares), combined with simple methods to exploit spatial as well as temporal redundancies typically present in biomedical signals. The algorithms are tested with publicly available electroencephalogram and electrocardiogram databases, surpassing in all cases the current state of the art in near-lossless and lossless compression ratios.
本文提出了用于多通道生物医学信号的无损和近无损压缩算法。这些算法具有顺序性且高效，这使其适用于低延迟和低功耗信号传输应用。我们利用信息论和信号处理工具（如通用编码、通用预测以及多元递归最小二乘法的快速在线实现），并结合简单方法来利用生物医学信号中通常存在的空间和时间冗余。这些算法通过公开可用的脑电图和心电图数据库进行测试，在所有情况下，其近无损和无损压缩比均超过了当前的技术水平。
