Efficient sequential compression of multi-channel biomedical signals

TL;DR

This paper introduces efficient lossless and near-lossless sequential compression algorithms for multi-channel biomedical signals, leveraging information theory and signal processing to improve compression ratios while maintaining low latency and power consumption.

Contribution

It presents novel sequential algorithms that exploit spatial and temporal redundancies in biomedical signals, outperforming existing methods in compression efficiency.

Findings

Surpasses current state-of-the-art in compression ratios.

Effective for low-latency, low-power applications.

Validated on EEG and ECG databases.

Abstract

This work proposes lossless and near-lossless compression algorithms for multi-channel 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.