Compression Spectrum: Where Shannon meets Fourier

TL;DR

This paper introduces the Compression Spectrum, a novel method combining signal processing and information theory by using a lossless compression algorithm to analyze the information content of signals across scales.

Contribution

It proposes a new tool called the Compression Spectrum that estimates signal compressibility at different scales, bridging spectral analysis and information theory.

Findings

Healthy young RR signals exhibit 1/f noise behavior in the Compression Spectrum.

Healthy old RR signals show different spectral behavior, indicating age-related changes.

The method is demonstrated on both synthetic and real-world signals, including heart rate data.

Abstract

Signal processing and Information theory are two disparate fields used for characterizing signals for various scientific and engineering applications. Spectral/Fourier analysis, a technique employed in signal processing, helps estimation of power at different frequency components present in the signal. Characterizing a time-series based on its average amount of information (Shannon entropy) is useful for estimating its complexity and compressibility (eg., for communication applications). Information theory doesn't deal with spectral content while signal processing doesn't directly consider the information content or compressibility of the signal. In this work, we attempt to bring the fields of signal processing and information theory together by using a lossless data compression algorithm to estimate the amount of information or `compressibility' of time series at different scales. To this end, we employ the Effort-to-Compress (ETC) algorithm to obtain what we call as a Compression Spectrum. This new tool for signal analysis is demonstrated on synthetically generated periodic signals, a sinusoid, chaotic signals (weak and strong chaos) and uniform random noise. The Compression Spectrum is applied on heart interbeat intervals (RR) obtained from real-world normal young and elderly subjects. The compression spectrum of healthy young RR tachograms in the log-log scale shows behaviour similar to $1/f$ noise whereas the healthy old RR tachograms show a different behaviour. We envisage exciting possibilities and future applications of the Compression Spectrum.