CEPS

CEPS analyzes complexity and entropy in physiological time-series to quantify physiological variability across signals such as heart rate variability, electroencephalography (EEG), postural sway, and temperature.

Key Features:

• Complexity estimation: Implements ten distinct methods for estimating data complexity, including fractal-based measures.
• Entropy measures: Implements 28 entropy measures covering established and recently introduced algorithms.
• Implemented measures: Includes specific measures such as Higuchi fractal dimension among the implemented complexity/entropy metrics.
• Signal types: Applicable to a wide range of physiological time series including heart rate variability, EEG, postural sway, and temperature recordings.
• Data pre-processing and parameter estimation: Provides pre-processing routines and ancillary methods for estimating embedding dimension (m) and time delay (τ) required for complexity and entropy calculations.

Scientific Applications:

• Paced-breathing pilot study: Applied to data from nine healthy adults to analyze effects of paced breathing and shown to differentiate breathing states more effectively than conventional linear, time- and frequency-domain measures.
• Physiological variability analysis: Used to detect that most complexity and entropy measures decreased during paced breathing at 7 breaths per minute while Higuchi fractal dimension increased.
• Clinical and biomedical research: Employed for investigations of physiological variability across multiple signal modalities in clinical and biomedical contexts.

Methodology:

Implements ten complexity-estimation methods and 28 entropy measures, with data pre-processing and parameter-estimation procedures for embedding dimension (m) and time delay (τ).