Assessment of time irreversibility in a time series using visibility graphs.

In this paper, we studied the time-domain irreversibility of time series, which is a fundamental property of systems in a nonequilibrium state. We analyzed a subgroup of the databases provided by University of Rochester, namely from the THEW Project. Our data consists of LQTS (Long QT Syndrome) patients and healthy persons. LQTS may be associated with an increased risk of sudden cardiac death (SCD), which is still a big clinical problem. ECG-based artificial intelligence methods can identify sudden cardiac death with a high accuracy. It follows that heart rate variability contains information about the possibility of SCD, which may be extracted, provided that appropriate methods are developed for this purpose. Our aim was to assess the complexity of both groups using visibility graph (VG) methods. Multivariate analysis of connection patterns of graphs built from time series was performed using multiplex visibility graph methods. For univariate time series, time irreversibility of the ECG interval QT of patients with LQTS was lower than for the healthy. However, we did not observe statistically significant difference in the comparison of RR intervals time series of the two groups studied. The connection patterns retrieved from multiplex VGs have more similarity with each other in the case of LQTS patients. This observation may be used to develop better methods for SCD risk stratification.

Heart rate variability, multifractal multiscale patterns and their assessment criteria.

OBJECTIVE: Both the central nervous system and the autonomic nervous system are complex physiological networks which modulate the heart rate. They are spatially extended, have built-in delays and work on many time scales simultaneously-nonhomogeneous networks with multifractal dynamics. The object of our research was the analysis of human heart rate variability (HRV) using the nonlinear multiscale multifractal analysis (MMA) method for several cardiovascular diseases. The analysis of HRV (night-time recordings) involved six groups of patients: 61 healthy persons, 104 cases with aortic valve stenosis, 42 with hypertrophic cardiomyopathy, 36 with atrial fibrillation, 70 patients with coronary artery disease and 19 with congestive heart failure. 85% of patients formed a training data set (282 subjects) and 15% formed a test data set (50 subjects). APPROACH: Multiscale multifractal analysis allows one to analyze the complexity of HRV and find the scaling properties of its fluctuations. The main result of MMA is the Hurst surface, the shape of which changes depending on the medical case analyzed. We prepared six criteria to distinguish a multifractal pattern for healthy subjects. We also prepared additional criteria, enabling one to recognize atrial fibrillation. MAIN RESULTS: For the training data set, we obtained the following accuracy statistics in distinguishing the patients from the healthy: 68% for coronary artery disease, 67% for hypertrophic cardiomyopathy, 88% for atrial fibrillation, 74% for aortic valve stenosis and 83% for congestive heart failure. For the complete training data set we obtained an accuracy of 73%, and 80% for the test data set (mean for ten random selections of the test data set). SIGNIFICANCE: The results of MMA presented here provide an additional input into the diagnostic process and may help to create a paradigm for future studies on medical screening methods, especially in that MMA focuses on very low frequencies of HRV not easily accessible by standard medical techniques. Satisfactory statistics for screening using both MMA and the unfiltered version of LF/HF indicate that the nature of the complete network moderating heart rhythm needs to be studied and that sinus rhythm in clinical patients may not always be separated from arrhythmia when its incidence is large.

Analysis of complex physiological systems by information flow: a time scale-specific complexity assessment.

In the last two decades conventional linear methods for biosignal analysis have been substantially extended by non-stationary, non-linear, and complexity approaches. So far, complexity is usually assessed with regard to one single time scale, disregarding complex physiology organised on different time scales. This shortcoming was overcome and medically evaluated by information flow functions developed in our research group in collaboration with several theoretical, experimental, and clinical partners. In the present work, the information flow is introduced and typical information flow characteristics are demonstrated. The prognostic value of autonomic information flow (AIF), which reflects communication in the cardiovascular system, was shown in patients with multiple organ dysfunction syndrome and in patients with heart failure. Gait information flow (GIF), which reflects communication in the motor control system during walking, was introduced to discriminate between controls and elderly patients suffering from low back pain. The applications presented for the theoretically based approach of information flow confirm its value for the identification of complex physiological systems. The medical relevance has to be confirmed by comprehensive clinical studies. These information flow measures substantially extend the established linear and complexity measures in biosignal analysis.

Assessment of the RR versus QT relation by a new symbolic dynamics method. Gender differences in repolarization dynamics.

A new method based on symbolic dynamics was applied to assess RR-QT dynamics and to compare gender differences. Segments of 10,000 RR and QT from the night were selected. The values of RR and QT were coded as follows. Each RR and QT interval was compared with their means in the last 50 beats [xRR, xQT]; when the interval was larger than x + delta then it was coded as a "2", where delta is the tolerance parameter; when it was less than x - delta-the code was a "0"; when it was larger than x-delta and and the less than x+delta-then it was coded as a "1." The tolerance parameter "delta" was equal to 10 ms for RR and 4 ms for QT. We obtained pairs of symbols representing the values of RR and QT-symbolic words. The results were presented in form of the probability density of the symbolic words. Mean RR, mean QT, SDRR, SDQT, QTc (Bazett formula) were also calculated. Electrocardiogram data of healthy individuals: 20 women and 20 men (mean age 39 +/- 12) were analyzed. There were significant gender differences in RR-QT dynamics. During heart rate acceleration the probability of QT shortening (the probability of the word "00") was higher in men than in women (P =.003). During heart rate deceleration QT lengthening (the word "22") was more frequently observed in men than in women (P =.003) as well. The QT reaction to RR interval changes is less complex in women than in men. In discriminant analysis, when QTc was ignored in the model, the RR-QT dynamics separated genders with 67% accuracy (chi(2) = 9.1, P <.003). RR-QT dynamics can be analyzed with symbolic dynamics methods. The gender differences in repolarization are not only due to QTc duration alone but also result from the dependence of the duration of QT on the RR duration.