fluctuations of the human heart beat constitute a complex system that has been studied mostly under resting conditions using conventional time series analysis methods. During physical exercise, the variability of the fluctuations is reduced, and the time series of beat-to-beat RR intervals (RRIs) become highly non-stationary. Here we develop a dynamical approach to analyze the time evolution of RRI correlations in running across various training and racing events under real-world conditions. In particular, we introduce dynamical detrended fluctuation analysis and dynamical partial autocorrelation functions, which are able to detect real-time changes in the scaling and correlations of the RRis as functions of the scale and the lag. We relate these changes to the exercise intensity quantified by the heart rate (HR). Beyond subject-specific HR thresholds the RRIs show multiscale anticorrelations with both universal and individual scale-dependent structure that is potentially affected by the stride frequency. These preliminary results are encouraging for future applications of the dynamical statistical analysis in exercise physiology and cardiology, and the presented methodology is also applicable across various disciplines. The increasing popularity and accuracy of wearable devices and sensors present new opportunities to study human physiology in a continuous, non-invasive manner for a huge number of subjects under real-world conditions. These devices enable the measurement of a plethora of physiological and mechanical signals such as the heart rate, beat-to-beat (RR) intervals, overall motion via GPS, motion of specific body locations via accelerations, and skin temperature. These data can be recorded in real time, often at 1 s intervals, and uploaded to web services. To date, most recorded data are not analyzed in scientific rigour due to a lack of suitable models for the dynamics of physiological signals under various intensities of exercise load, and also due to restricted availability of the data (property of industry and users). This limits opportunities for a better understanding of complex physiological processes, diagnostics and monitoring for patients in rehabilitation, and the optimal training of athletes. However, it has been long known that a variety of physiological conditions and cardiac diseases affect heart rate variability (HRV) and the correlations in RR intervals 1. In exercise physiology, HRV is often used at rest to evaluate recovery, fatigue and overtraining. It is known that during exercise the overall variability of the RR intervals (RRI) is strongly suppressed. Regardless, the RRI correlations contain valuable information even during exercise 2-4. For example, the possibility to determine certain physiological thresholds, such as the anaerobic threshold, from the frequency spectrum of HRV has been examined 5,6. Often the relative importance of lowfrequency (LF: 0.04-0.15 Hz) and high-frequency (HF: 0.15-0.4 Hz) spectral power is studied during exercise. Using this concept as a measure of t...
We assess the feasibility of heart rate variability (HRV) estimated from interbeat interval (IBI) data measured with wrist-worn photoplethysmography device for sleep stage classification. In particular, we examine fractal correlations in the IBIs as the function of both time and scale.Optical heart rate sensor by PulseOn Ltd was utilized for monitoring IBIs from 18 healthy young adult subjects. Reference ambulatory polysomnography recordings were scored by a sleep physician. The HRV was studied by detrended fluctuation analysis by computing scale-dependent spectra of scaling exponents α(s). Dynamic changes were tracked by calculating the spectra α(s, t) in moving temporal windows whose length varied with the scale.The dynamic landscapes of the alpha spectra show distinctive fractal correlations according to the underlying sleep stages. Respiratory effects, blood pressure variations, and thermoregulatory influence appear to be discernible as well. Classification of the alpha spectra yields up to 73 %, 60 % and 54 % average accuracies for 3-class (wake, REM, NREM), 4-class (wake, REM, N1+2, N3) and 5-class (wake, REM, N1, N2, N3) cases, respectively.
Detrended fluctuation analysis is a popular method for studying fractal scaling properties in time series. The method has been successfully employed in studying heart rate variability and discovering distinct scaling properties in different pathological conditions. Traditionally the analysis has been performed by extracting two scaling exponents from linear fits, for short-and long-range correlations respectively. The extent of these ranges is subjective and the linear two-range model potentially disregards additional information present in the data. Here we present a method based on the Kalman smoother for obtaining a whole spectrum of scaling exponents as a function of the scale. Additionally, we present an optimization scheme to obtain data-adaptive segmentation of the fluctuation function into approximately linear regimes. The methods are parameter-free and resistant to statistical noise in the fluctutation function. We employ the methods in the analysis of the heart rate variability of patients with different heart conditions. The methods enhance the classification of these conditions, revealing more complex structure in the scaling exponents beyond the two-range model.
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