title: Efficient Sequential Compression of Multichannel Biomedical Signals.
abstract: 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.
authors.forename: Ignacio
authors.initials: I
authors.lastname: Capurro
authors.forename: Federico
authors.initials: F
authors.lastname: Lecumberry
authors.forename: Alvaro
authors.initials: A
authors.lastname: Martin
authors.forename: Ignacio
authors.initials: I
authors.lastname: Ramirez
authors.forename: Eugenio
authors.initials: E
authors.lastname: Rovira
authors.forename: Gadiel
authors.initials: G
authors.lastname: Seroussi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D044962:Data Compression / Q000379:methods*; D004562:Electrocardiography / Q000145:classification; D004569:Electroencephalography / Q000145:classification*; D006801:Humans; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted