# Frequency Band Analysis of Multiple Stationary Time Series

## TL;DR

This paper introduces a data-driven method to identify optimal frequency bands in biomedical signals, revealing subpopulations with distinct patterns.

## Contribution

A novel data-adaptive approach for frequency band analysis that identifies subpopulations and preserves spectral variability.

## Key findings

- The method identifies frequency bands that preserve spectral differences across participants.
- Genetic algorithms effectively detect subpopulations with distinct power spectra.
- Analysis of stride intervals reveals subpopulations with neurological disorder-specific patterns.

## Abstract

The frequency‐domain properties of biomedical signals offer valuable insights into health and functioning of underlying physiological systems. The power spectrum, which characterizes these properties, is often summarized by partitioning frequencies into standard bands and averaging power within bands. These summary measures are regularly used for analysis in practice, but are not guaranteed to optimally retain differences in power spectra across signals from different participants. We propose a data‐adaptive method for identifying frequency band summary measures that preserve spectral variability within a population of interest. The method can also identify subpopulations with distinct power spectra and summary measures that best characterize local dynamics. Numerical selection criteria are developed to select a reasonable number of bands and subpopulations that best characterize overall dynamics. A genetic algorithm is designed to simultaneously identify subpopulations and their corresponding summary measures. The method is used to analyze stride interval series from patients with different neurological disorders, revealing distinct subpopulations and the need for subpopulation‐dependent summary measures.