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Homeobios: The Pattern of Heartbeats in Newborns, Adults, and Elderly Patients

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Abstract

HRV has been found useful in the study of cardiological illness in adults and elders, as well as in monitoring prenatal health. Twenty-four hour Holter recordings of R to R intervals (RRI) in healthy newborns, adults, and elderly persons were analyzed with statistical, chaos, and recurrence methods. In persons of all ages, RRI series showed relative stability (as expected in homeostatic regulation), patterned daily changes in heart rate, evidence of causality or 'determinism' (nonrandom pattern of the series of differences), and non-periodic irregular variations within limits, suggesting chaos. In addition, novel methods of analysis reveal creative features that are absent in chaotic attractors but found in bios, a non-stationary process that is generated mathematically by recursions of bipolar feedback (chaotic bios) or by the addition of sine waves. Wavelet and recurrence plots demonstrate time-limited patterns (e.g. clustering of recurrences in organized complexes) that follow each other in time indicating temporal complexity, in contrast to the temporal uniformity of chaotic attractors and of random changes. Recurrence quantification demonstrates less recurrence isometry than copies randomized by shuffling (novelty), and more consecutive isometries than shuffled copies indicating causal order. Statistical analyses demonstrate asymmetric distribution and diversification (increase in variance with the duration of the series analyzed) in contrast to convergence to an attractor. These studies indicate that the normal pattern of HRV is both homeostatic and biotic. A biotic pattern with homeostatic features (homeobios) is generated by combining bipolar feedback with negative feedback. Chaos and bios analyses may thus be useful in clinical studies.
... As per the theorem, whenever the dynamics of a system depends on a number of variables and the information about only one variable (say x) is available, the values of the measured variable can be plotted against themselves several times at a specific time delay to reconstruct the complete dynamics of the system (Takens, 1981;Wallot, Roepstorff, & Mønster, 2016). Recurrence plots are useful to describe the variation in the RRI patterns with time (Hector Sabelli & Lawandow, 2010). It has been reported earlier that the recurrence-rich regions represent a stable heart rate, whereas, the recurrence-free regions indicate the acceleration or deceleration of the heart rate (Hector Sabelli & Lawandow, 2010). ...
... Recurrence plots are useful to describe the variation in the RRI patterns with time (Hector Sabelli & Lawandow, 2010). It has been reported earlier that the recurrence-rich regions represent a stable heart rate, whereas, the recurrence-free regions indicate the acceleration or deceleration of the heart rate (Hector Sabelli & Lawandow, 2010). In this study, recurrence plot was drawn for each volunteer in both the pre-and the post-stimulus conditions. ...
... In the recurrence analysis, a sequence of points of the time series data (i.e., a vector) is used to represent a single point in the phase space. The number of the time series data points in this vector is regarded as the embedding (Hector Sabelli & Lawandow, 2010). Isometry is the recurrence of the vectors, the difference of whose Euclidean norms are below a cut-off radius (H Sabelli et al., 2005). ...
Chapter
This chapter has been inspired by the need to find an efficient method to study the changes in the cardiac autonomic regulation before and after smoking in young smokers. Appropriate processing and analysis of the RR intervals, extracted from the electrocardiogram (ECG) signals, is an important non-invasive tool to determine the autonomic nervous system (ANS)-based alteration of the cardiac physiology. The authors propose to analyze the RR interval signals by recurrence analysis and extracting the features thereof. In total, 17 young volunteers (smokers) participated in the study. The ECG signals were acquired before and after smoking. The statistical features obtained from the recurrence analysis were used as the input to artificial neural network (ANN)-based classification so as to classify the basal state from the post-stimulus state. A classification efficiency of 100% could be achieved using the recurrence features of the RR interval signals. Thus, the results evince that the proposed method is an efficient method to study the changes in the RR interval signal due to smoking.
... The recurrence of the same length (i.e., equal Euclidean norm within the specified tolerance) vectors in the phase space diagram of a dynamic system divulges information about the isometry. It is measured as the number of recurrence isometries expressed as a percentage of the entire number of pairs of vectors of the sample (Hector Sabelli & Lawandow, 2010).The plot of isometry or any of its derived parameters (expressed in %) with embedding (i.e., the dimensionality of vectors in the phase space diagram) is regarded as an embedding plot. In the current study, the embedding plots of isometry for the pre-and the post-stimulus conditions suggested that the isometry of the pre-stimulus condition was more than that of the post-stimulus condition ( Figure 1a). ...
... In the current study, the embedding plots of isometry for the pre-and the post-stimulus conditions suggested that the isometry of the pre-stimulus condition was more than that of the post-stimulus condition ( Figure 1a). This, in turn, meant that the pre-stimulus condition showed more patterned behaviour than that of the post-stimulus condition(Hector Sabelli & Lawandow, 2010). ...
... Recurrence plot is the graph of the isometry as a function of time that represents the temporal variations in the pattern. The diagonal line (from the lower left corner to the upper right corner) represents the time (Hector Sabelli & Lawandow, 2010). Sabelli et al. (2010) have investigated that the acceleration and deceleration of cardiac rate are depicted by recurrence-free regions, while the stable cardiac rate is represented by recurrence-rich regions in the recurrence plot of RRI time series (Hector Sabelli & Lawandow, 2010). ...
Chapter
This chapter investigates the effect of a motivational song (stimulus) on the physiology of the autonomic nervous system and the electrical activity of the heart. Five min electrocardiogram (ECG) signals were acquired from 19 volunteers during the resting and the post-stimulus conditions. The RR intervals (RRIs) were extracted. Recurrence analysis of the RRI time series indicated a higher alteration (acceleration or deceleration) in the heart rate along with the reduction of the causality and patterned behavior of the RRIs. The exact alteration in the ANS physiology was examined using heart rate variability (HRV) analysis. The results of the HRV analysis suggested an increase in the parasympathetic activity in the post-stimulus condition. The alteration in the cardiac activity was analyzed using time domain and joint time-frequency domain analyses of ECG signals. The results suggested an alteration in the cardiac electrical activity of the heart in the post-stimulus condition.
... Cardiovascular signals have been reported to have discontinuous alterations in between deterministic trajectories and stochastic processes (Sabelli & Lawandow, 2010;Sabelli et al., 2011;Sabelli et al., 2005). Recurrence analysis is one of the important tools for analyzing non-deterministic complexity, as seen in cardiovascular signals (Sabelli et al., 2005). ...
... In this study, the recurrence parameters, namely, isometry, consecutive isometry, radial isometry, consecutive radial isometry, and novelty were analyzed along with recurrence plot to find out any change in the RRI time series pattern due to the music stimulus. Isometry is defined as the recurrence of vectors having same length (Sabelli & Lawandow, 2010). This vector length is calculated from its Euclidean norm. ...
... Two vectors are regarded as isometric if the absolute value of the difference between their Euclidean norms is smaller than some cut-off radius. The number of time series points included in each data sequence is called embedding dimension (Sabelli & Lawandow, 2010). Embedding plots represent the variation in isometry or its derived parameters (e.g., consecutive isometry, radial isometry, consecutive radial isometry and novelty) as a function of embedding dimensions (Sabelli et al., 2005). ...
Chapter
In this chapter, the effect of an old generation romantic music (stimulus) on the autonomic nervous system (ANS) activity and the cardiac electrophysiology of Indian male volunteers was investigated. Electrocardiogram (ECG) signals were acquired and the corresponding RR intervals (RRIs) were extracted. The recurrence analysis of the RRI time series suggested a more stable heart rate in the post-stimulus condition. Heart rate variability (HRV) analysis detected a dominant parasympathetic activity in the post-stimulus condition. The time-domain and the wavelet transform analyses of the ECG signals predicted an alteration in the electrical activity of the heart because of the exposure to the music stimulus. The classification of the HRV and the ECG parameters was performed using artificial neural network (ANN), which resulted in an accuracy of =80%.
... The reduction in the value of the HR SD in Category-B as compared to Category-C suggested that the HR values are less spread out around the mean HR value in Category-B. This can be explained by the reduced HRV, which suggest a reduction in the dynamicity of the ANS [83]. It is well reported in the literature that the reduced HRV may lead to cardiological as well as non-cardiological illness [21]. ...
Article
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... Isometry is represented as the quantity of isometric recurrences articulated as a fraction of the entire quantity of pairs of vectors contrast in the sample (i.e. %isometry) [15]. The design of %isometry or any of its copied frameworks as a function of embedding dimension is considered as an embedding plot. ...
Chapter
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