This consensus statement has been compiled on behalf of the International Society for Holter and Noninvasive Electrophysiology. It reviews the topic of heart rate turbulence (HRT) and concentrates on technologies for measurement, physiologic background and interpretation, and clinical use of HRT. It also lists suggestions for future research. The phenomenon of HRT refers to sinus rhythm cycle-length perturbations after isolated premature ventricular complexes. The physiologic pattern of HRT consists of brief heart rate acceleration (quantified by the so-called turbulence onset) followed by more gradual heart rate deceleration (quantified by the so-called turbulence slope) before the rate returns to a pre-ectopic level. Available physiologic investigations confirm that the initial heart rate acceleration is triggered by transient vagal inhibition in response to the missed baroreflex afferent input caused by hemodynamically inefficient ventricular contraction. A sympathetically mediated overshoot of arterial pressure is responsible for the subsequent heart rate deceleration through vagal recruitment. Hence, the HRT pattern is blunted in patients with reduced baroreflex. The HRT pattern is influenced by a number of factors, provocations, treatments, and pathologies reviewed in this consensus. As HRT measurement provides an indirect assessment of baroreflex, it is useful in those clinical situations that benefit from baroreflex evaluation. The HRT evaluation has thus been found appropriate in risk stratification after acute myocardial infarction, risk prediction, and monitoring of disease progression in heart failure, as well as in several other pathologies.

The geometry of the Poincaré plot of RR intervals is considered and its basic descriptors are defined in terms of the second moment of a distribution of points in a plane. One of the standard descriptors, SD1, is redefined and used to define two new descriptors, SD1(UP) and SD1(DOWN), whose squares partition SD1(2) (the variance corresponding to short-term heart rate variability) into contributions from decelerations and accelerations of heart rate. It is shown that there is a visible and statistically highly significant asymmetry in the Poincaré plot, with the upper part, corresponding to decelerations of heart rate, larger than the lower part, which corresponds to accelerations. The effect is shown in one hundred 30 min long time series of RR intervals derived from the ECG recordings of 100 young (19-32 years old) and healthy adults. After shuffling the data to random order the asymmetry disappears, which shows that this is a genuine physiological phenomenon rather than an artefact of the method.

The Poincaré plot is a widely used method for visualizing and calculating heart rate variability and for investigating the oscillatory nature of heart action. We show that the Poincaré plot produced using physiological data for RR intervals is asymmetric. This suggests that the processes of heart rate acceleration (shortening of consecutive RR intervals) and deceleration (prolongation of successive RR intervals) might be asymmetric. To investigate this phenomenon, we define descriptors quantifying the heart rate asymmetry and present the results of a study involving 5-min ECG recordings of 50 healthy subjects in which, despite of the shortness of the recordings, the asymmetry is clearly visible.

Aim: To analyze the correlation of the Poincaré plot descriptors of RR intervals with standard measures of heart rate variability (HRV) and spontaneous baroreflex sensitivity (BRS). A physiological model of changing respiratory rates from 6 to 15 breaths/min provided a wide range of RR intervals for analysis. Material and methods: Beat-to-beat finger blood pressure, ECG, and respiratory curves were recorded noninvasively in 15 young healthy volunteers (19-25 years old; 7 females) breathing for 5 min at 4 different respiratory rates of 6, 9, 12, and 15 breaths/ min. Four descriptors of the Poincaré plot (SD1, SD2, S, and SD2/SD1), time and frequency domain HRV, and spontaneous BRS (cross-correlation method) were calculated for each 5-min recording. Results: The values of SD1 characterizing short-term HRV, SD2 describing long-term HRV, and S measuring total HRV were significantly correlated with BRS and time and frequency domain measures of short, long, and total HRV. The LF/ HF significantly correlated with SD2 and SD2/SD1 representing the balance between long-and short-term HRV. None of the Poincaré plot descriptors was correlated with the mean RR interval. The increased respiratory rate caused a significant reduction of BRS, measures of total and long-term HRV, and an increase of HF that peaked at 12 breaths/min. Conclusions: The descriptors of the Poincaré plot of RR intervals are significantly correlated with measures of BRS and time and frequency domain HRV, but not with heart rate. A faster respiratory rate reduces long-term HRV measures and temporarily increases HF.Key words: Poincaré plot, heart rate variability, baroreflex sensitivity, respiratory sinus arrhythmia, paced breathing.The measurement of heart rate variability (HRV) is a valuable tool in both clinical practice and physiological research [1,2]. The assumption is that variability is inherent in heart rate, reflecting the ability of the cardiovascular system to adapt to external and internal changes. Multiple studies show that HRV is reduced in various diseases and old age. Indeed, reduced HRV has proven valuable in predicting mortality in the survivors of myocardial infarction [2,3]. In spite of its usefulness, there is no single accepted measure of HRV [1,3].The Poincaré plot of RR intervals is one of the recent methods of HRV analysis. It has also been used to measure the autonomic modulation and randomness of the heart rate [1,[4][5][6][7][8][9][10][11][12]. The Poincaré plot is a graphical representation of temporal correlations within the RR intervals derived from ECG [4,5]. In this plot (Fig. 1), each RR interval is a function of the preceding RR interval, i.e., the duration of the current cardiac beat (RR n ) is represented on the x axis, and the duration of the following beat (RR n+1 ) on the y axis, so each point (RR n , RR n+1 ) in the plot corresponds to two successive heart beats. Various descriptors are associated with this plot, some of which have a convincing physiological interpretation [5,6].In the present study we ai...

We report on two new physiological phenomena: the long-term and total heart rate asymmetry, which describe a significantly larger contribution of heart rate accelerations to long-term and total heart rate variability. In addition to the existing pair of indices, which are based on partitioning short-term variance, we introduce two other pairs of descriptors based on partitioning long-term () and total () heart rate variability. The new asymmetric descriptors are used to analyze RR intervals time series derived from the 30-min ECG recordings of 241 healthy subjects resting in supine position. It is shown that both new types of asymmetry are present in 76% of the subjects. The new phenomena reported here are real physiological findings rather than artifacts of the method since they vanish after data shuffling.

The Poincaré plot (PP) is one of the many techniques used for ascertaining the heart rate variability, which in turn is a marker of the activity of the autonomic system. Poincaré plots are very simple to produce, but their preparation involves a few fine points. This paper describes one of them, namely the filtering of data used for the Poincaré plot. We show the correct way of filtering data, present a few results of not filtering or incorrect filtering and demonstrate how proper filtering helps extract interesting information from the data. A few algorithms for preparing Poincaré plots, filtering data and calculating PP descriptors are included. As Matlab's programming language is the unquestionable standard for data analysis in the medical sciences, we illustrate these algorithms by snippets of code in this language.

Smartphones, mobile applications ('apps'), social media, analytics, and the cloud are profoundly changing the practice of medicine and the way health decisions are made. With the constant progress of technology, the measurement of vital signals becomes easier, cheaper, and practically a standard approach in clinical practice. The interest in measuring vital signals goes beyond medical professionals to the general public, patients, informal caregivers, and healthy individuals, who frequently lack any formal medical training. On smartphone platforms such as iOS and Android, a proliferation of health or medical 'apps' acquire and analyse a variety of vital signs through embedded sensors, interconnected devices or peripherals utilising on occasion analytics and social media. Smartphone vendors compete with traditional medical device manufacturers in the grey area between health care, wellness, and fitness, as US and EU regulatory bodies are setting and revising rules for these new technologies. On the other hand, in the absence of robust validation results, clinicians are hesitant to trust measurements by apps or recommend specific apps to their patients, partly also due to lack of a cost reimbursement policy. This review focuses on the acquisition and analysis on smartphones of three important vital signs in the cardiovascular and respiratory field as well as in rehabilitation i.e. heart or pulse rate, blood pressure, and blood oxygenation. The potential, pitfalls, and perspectives on mobile devices and smartphone apps for health management by patients and healthy individuals are discussed.

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