Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy

Abstract

Background: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. Methods: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190–220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). Results: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1–5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. Conclusion: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.

A novel stretching and folding characterization method based on geometrical and physiological traits of chaotic and intermittent tracking signals

“…The accurate and precise results were attained by combining Higuchi's FD and other nonlinear features [54,58]. The combination of Higuchi's FD and CD was employed for better evaluation of muscle fatigue [77], or better differentiation of multilevel emotions [58], breathing rate [78], and disease states in multiple sclerosis, Alzheimer's and Parkinson's disease [79]. Consequently, in this study, both FD and CD were used for better investigation and comparison of tracking dynamics among different tracking tasks.…”

Dispersion complexity-entropy curves: an effective method to detect the structures of complex systems

“…Measuring the complexity degree of a system is a common problem in nonlinear dynamic areas since it can help people to discover the inner structures and characteristics of systems. During the last several decades, researchers have proposed lots of methods to define complexity such as algorithmic complexities [1], fractal dimensions [2], Lyapunov exponents [3], and other nonlinear time series methods [4] and they are widely applied in many fields such as physics, computer science or biomedicine [5][6][7][8][9]. However, these methods have a common disadvantage in that they are too sensitive to tuning parameters, which may add difficulties in calculation and analysis.…”