Rate Variability Data Research Articles

Phase transitions detected in complex time series by multifractal detrended fluctuation analysis

A distinctive fishtail feature in multifractal spectra derived from heart rate variability (HRV) data closely resembles similar features in plots of the Gibbs free energy versus reduced pressure for a van der Waals fluid below the critical temperature, and, in analogy to the nonideal fluid, signals a phase transition from one multifractal state to another. This fishtail feature, often overlooked or dismissed in previous studies, is observed most prevalently in unhealthy patients and suggests a realignment between multifractal variables and their thermodynamic analogs. The van der Waals analogy and subsequent alternate interpretation of multifractal variables and functions lead one to construct a three-dimensional heart health phase diagram reminiscent of a [Formula: see text] diagram for the van der Waals fluid. The diagram which makes use of the Maxwell width as a measure of heart health produces an orderly progression from healthy to unhealthy.

Examining the influence of emotional arousal and scam preventive messaging on susceptibility to scams

With the increase in scams globally and the elusive methods of perpetrators, law enforcement agencies have turned to public education and awareness programs to decrease the number of scam victims. This has also raised a need to look into the psychology of scams and how they can be prevented. Emotional arousal has been shown to hinder cognitive decision-making processes in scam victims, subsequently influencing them to fall prey to scams. Despite this, messages used in scam prevention campaigns have been framed in ways that appeal to rational cognitive processes. This exploratory study examined two research questions: do (a) the type of messaging used in scam prevention posters (cognitive-focused poster vs. emotion-normalising poster) and (b) emotional arousal (positive arousal vs. negative arousal vs. no arousal) influence one’s susceptibility to scams? Susceptibility to scams was measured through participants’ intention to purchase items during a fake e-commerce scam scenario. Emotional arousal was measured with a combination of heart rate variability (HRV) data obtained through wearable heart rate trackers, and self-report scales. Results of the study showed that participants who viewed emotion-normalising posters demonstrated a lower susceptibility to scams compared to those who viewed the cognitive-focused poster. However, emotional arousal was not found to have any influence on one’s susceptibility to scams. These findings serve to inform crime prevention campaigns by law enforcement. Findings from this exploratory study can also encourage further research into the scam prevention research.

From Individual Output to Pooled Data

Abstract. This technical note introduces an easy-to-use macro in Office Excel to batch the output from Kubios HRV 2.2, which is a software program for heart rate variability (HRV) data extraction. Due to a new release of the program, previous solutions have become outdated. The current macro simplifies and substantially reduces the time needed to post-process the output from Kubios HRV 2.2. With the use of this macro, advancements in knowledge on the role of HRV in mental and physical health can be accelerated.

Comparison of Heart Rate Variability Parameters to the Autonomic Reflex Screen in Postural Orthostatic Tachycardia Syndrome and Neurogenic Orthostatic Hypotension.

The clinical significance of heart rate variability (HRV) in the context of autonomic dysfunction continues to be a matter of debate. Therefore, the purpose of the current study was to investigate the clinical relevance of HRV in the context of autonomic dysfunction. Heart rate variability data from 225 volunteers consisting of controls (n = 166) and patients with mild (n = 25) and severe (n = 34) autonomic dysfunction were retrospectively analyzed. Time and frequency parameters were correlated against baseline and standardized tests of autonomic function. During baseline, resting HR was negatively correlated with time (SD of all normal RR interval, r = -0.511; RMSSD, r = -0.585; pNN50, r = -0.545) and frequency (low-frequency, r = -0.362; high-frequency, r = -0.421) parameters (P < 0.01). Resting systolic blood pressure demonstrated similar significant correlations (P < 0.01). During head-up tilt, SD of all normal RR intervals was positively correlated with [INCREMENT]HR and change in systolic blood pressure (r = 0.340; r = 0.538, respectively; P < 0.01). Similarly, low-frequency, high-frequency, and low-frequency/high-frequency ratios were correlated with [INCREMENT]HR (r = 0.422, r = 0.176, r = 0.470) and change in systolic blood pressure (r = 0.451, r = 0.407, and r = 0.185) (P < 0.01). Time parameters (SD of all normal RR intervals, RMSSD, and pNN50) were all significantly correlated with deep breathing (r = 0.600; r = 0.556; r = 0.516; P < 0.01, respectively). Low-frequency and high-frequency power were also correlated (r = 0.596; r = 0.580, respectively) (P < 0.01). Time and frequency parameters showed significant negative correlations with baseline hemodynamics. During a test of sympathetic activation and parasympathetic withdrawal, this relationship shifted to reveal significant positive correlations between HRV parameters and hemodynamics. Last, during a test of parasympathetic activation, there were significant positive correlations with cardiovagally mediated HRV parameters. Overall, these results suggest broader clinical relevance for HRV parameters within the spectrum of autonomic functioning.

Influence of Type 1 Diabetes on the Symbolic Analysis and Complexity of Heart Rate Variability in Young Adults

BackgroundType 1 diabetes mellitus can cause autonomic changes, which can be assessed by heart rate variability. Among the heart rate variability assessment methods, the symbolic analysis and Shannon entropy, based on the Chaotic dynamics, have gained prominence.ObjectiveTo compare heart rate variability indexes, obtained through symbolic analysis and Shannon entropy, in young adults with type 1 diabetes mellitus and healthy young individuals, associated with the analysis of linear indexes; and to verify if there are associations between the indexes obtained by the symbolic analysis and by Shannon entropy and linear indexes in diabetic individuals.MethodsHeart rate variability data from 39 young adults with type 1 diabetes mellitus and 43 healthy young individuals were analyzed, using a cardio-frequency meter. Linear indexes (standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds; square root of the mean of the squared differences between adjacent normal RR intervals in a time interval expressed in milliseconds; low and high frequency components in millisecond squared; and normalized units and ratio between low and high frequency components) and nonlinear ones (Shannon entropy and symbolic analysis - standard without variation; with one or two variations; and with two different variations) of the heart rate variability were calculated. The statistical significance was set at 5%, and the confidence interval was 95%.ResultsSignificantly lower values were observed in the DM1 group compared to healthy young adults for the standard deviation indexes of all normal RR intervals recorded in a time interval [37.30 (29.90) vs. 64.50 (36.20); p = 0.0001], square root of the mean of the squared differences between adjacent normal RR intervals in a time interval [32.73 (17.43) vs. 55.59 (21.60); p = 0.0001], low frequency component [402.00 (531.00) vs. 1,203.00 (1,148.00); p = 0.0001], high frequency component [386.00 (583.00) vs. 963.00 (866.00); p = 0.0001] and the pattern with two different variations [15,33 (9,22) vs. 20.24 (12.73); p = 0.0114], with the effect of this difference being considered large (standard deviation of all normal RR intervals recorded in a time interval, square root of the mean of the squared differences between adjacent normal RR intervals in a time interval and low frequency component), medium (high frequency component) and small (standard with two different variations). The agreement of the associations between the linear and non-linear indexes was considered elevated for the high frequency component index - normalized units (r = -0.776), with the standard index without variation, and moderate for the indexes square root of the mean of the squared differences between adjacent normal RR intervals in a time interval (r = 0.550), standard deviation of all normal RR intervals recorded in a time interval (r = 0.522), high frequency component - normalized units (r = 0.638) with the index standard with two similar variations, as well as for the indexes square root of the mean of the squared differences between adjacent normal RR intervals in a time interval (r = 0.627) and high frequency component - normalized units (r = 0.601) with the index standard with two different variations.ConclusionType 1 diabetes mellitus influenced linear indexes and symbolic analysis, but not yet in the complexity of heart rate variability. Additionally, heart rate variability indexes correlated with the symbolic dynamics.

Influence of embedding dimension on distribution entropy in analyzing heart rate variability.

Distribution entropy (DistEn) is a recent measure of complexity that is used to analyze Heart Rate Variability (HRV) data. DistEn which is a function of data length N, number of bins M and embedding dimension m is known to be stable and consistent with respect to parameters N and M respectively. Also, (N, M) are known to have a combined effect in deciding performance of DistEn as a classification feature. But, all such analysis have mostly ignored the influence of the third parameter m on DistEn properties. Though a random fixed choice of m value has so far succeeded in portraying the effect of other parameters on DistEn, it is considered equally important to reveal the influence of a varying m on DistEn and its characteristics. This study examines the impact of m on the stability, consistency and performance of DistEn when the latter is used to analyze HRV data belonging to (i) healthy subjects discerned by age and (ii) subjects discerned by their heart's physiologic condition. Here, data length N of each signal is varied from 50 to 1000, while the number of bins M used varies from 100 to 2000. Information pertaining to m variations is obtained by carrying out experiments at four different values of embedding dimension; m = 2, 3,4 and 5. The study shows that the stability, consistency and classification performance of DistEn is not much influenced by changes in m.

Effect of embedding dimension on complexity measures in identifying Arrhythmia.

Entropy measures like Approximate entropy (ApEn) and Sample entropy (SampEn) are well established tools to analyze Heart Rate Variability (HRV) data. Critical parameters involved in these computations namely embedding dimension m and tolerance r are in most cases assumed to be 2 and 0.2*signal SD (standard devaition) respectively. Such assumptions do not work fairly across data sets and thus create misleading results in many cases. Problems with r have been addressed with the advent of newer entropy measures like Permutation entropy (PE), Fuzzy entropy (FuzzyEn) and Distribution entropy (DistEn) that simply eliminate, modify or replace r from calculations. On the other hand, the disadvantage of using a fixed assumed choice of m when such measures are used for data classification is yet to be investigated. The smallest variation in m may effect the extent of information retrieval from HRV data and hence it is extremely important to analyze different possibilities and outcomes of the same. In this study, we scrutinize the behavior of different entropy measures with regard to their classification performance at four different values of embedding dimension i.e., m = 2, 3,4 and 5. Normal and Arrhythmic RR intervals taken at data lengths ranging from 50 to 1000 have been used for the purpose. At any choice of m, DistEn and PE are the best measures to classify Arrhythmic data, whose AUC (Area under the ROC curve) values can go as high as 0.94 and 1 respectively. However PE performance becomes unstable with N for m > 3 (highest Δ being 0.3 at m = 5, Δ being the difference between minimum and maximum AUC). Irrespective of the choice of m, DistEn performance remains the most efficient and stable (highest Δ being only 0.03 at m = 4) for Arrhythmia classification. In the case of all other entropy measures, it is recommended that the value of m be chosen with discretion to ensure stability and efficiency in classification performance.

Cardiovascular effects of nocturnal aircraft noise on healthy volunteers

Background: Nighttime aircraft noise can impair sleep quality and may also increase blood pressure and the risk for myocardial infarction for persons in highly noise exposed residential areas. Pathophysiology and possible susceptibility factors for cardiovascular effects of aircraft noise remain unclear. Methods and results: 75 healthy volunteers (mean age 26 y) were studied for three nights in their homes. One control pattern without noise (Control) and two noise patterns with 30 (Noise 30) or 60 (Noise 60) aircraft noise events were played back in random and blinded order. After each study night flow-mediated dilatation (FMD) of the brachial artery was measured the following the morning. Blood pressure (BP), heart rate and actimetric data were recorded continuously during noise exposure. Peak sound pressure levels were 60 dB(A) for both patterns, with Leq(3)= 43.1±4.9 dB(A) for Noise 30 and Leq(3)= 46.3±3.9 dB(A) for Noise 60. FMD (%) dose dependently declined from 10.4%±3.8%; (Control) to 9.7%±4.1% (Noise30) and 9.5±4.3% (p=0.052) after the night with 60 noise events (Noise 60). Effects on FMD were particularly evident if Noise60 followed after Noise 30, suggesting a possible priming through anticipation. BP rose from 109.8±15.4 mmHg to 114.9±13.9 mmHg in Noise 30 and to 115.2±12.4 mmHg in Noise 60. Average heart rate, heart rate variability and actimetry data did not differ between patterns. Adrenaline concentration increased from 28.6±11.4 ng/l to 33.0±16.9 (Noise 30) and 34.0±19.0 ng/l (Noise 60; p = 0.0099). Serum cortisol remained unaffected by noise exposure. Conclusion: In a low risk population a short exposure to nocturnal aircraft noise can impair endothelial function, increase adrenaline concentrations and blood pressure. Exposure to aircraft noise in prior nights seems to augment this effect.

Effect of Chan-Chuang Qigong on Mind-Body Balance

The purpose of this research was to explore the effect of Chan-Chuang Qigong on mind-body balance of female elementary school staff. Forty one female staff members from Taipei elementary school were participated in this study. According to the quasi-experimental design and convenience sampling, subjects were divided into control (n = 21) and experimental (n = 20) groups. The experimental group was scheduled with the Qigong course, 2 times a week for 10 weeks. The researcher-designed mind-body balance questionnaire was verified with the reliability and validity tests. The ANSWatch monitor was used to collect the pre- and post heart rate variability (HRV) data. The obtained results were as follows: (1) The two-way mixed design 2 (experimental and control groups)×2 (pre- and post tests) ANOVA showed that there was an interaction between the groups and tests in the mind-body balance questionnaire. Whereas, no significant differences were found in the main effect analyses between experimental and control groups. The scores of mind-body balance questionnaire were significantly increased in experimental group, but decreased in control group. (2) Experimental group HRV measured at pre- and post tests showed no significant difference. (3) The LF/HF (low frequency/high frequency) ratio was significantly decreased in both groups (experimental from 0.96 to 0.51, control from 1.62 to 1.30). This study concludes that (1) the Chan-Chuang Qigong effect could be observed properly via mind-body balance questionnaire, (2) the Chan-Chuang Qigong was unable to improve the HRV, and (3) the Chan-Chuang Qigong could stabilize the sympathetic and parasympathetic nervous systems.

Personal Adaptive Method to Assess Mental Tension during Daily Life Using Heart Rate Variability

The aim of this study is to present a method of assessing psychological tension that is optimized to every individual on the basis of the heart rate variability (HRV) data which, to eliminate the influence of the inter-individual variability, are measured in a long time period during daily life. HRV and body accelerations were recorded from nine normal subjects for two months of normal daily life. Fourteen HRV indices were calculated with the HRV data at 512 seconds prior to the time of every mental tension level report. Data to be analyzed were limited to those with body accelerations of 30 mG (0.294 m/s²) and lower. Further, the differences from the reference values in the same time zone were calculated with both the mental tension score (Δtension) and HRV index values (ΔHRVI). The multiple linear regression model that estimates Δtension from the scores for principal components of ΔHRVI were then constructed for each individual. The data were divided into training data set and test data set in accordance with the twofold cross validation method. Multiple linear regression coefficients were determined using the training data set, and with the optimized model its generalization capability was checked using the test data set. The subjects' mean Pearson correlation coefficient was 0.52 with the training data set and 0.40 with the test data set. The subjects' mean coefficient of determination was 0.28 with the training data set and 0.11 with the test data set. We proposed a method of assessing psychological tension that is optimized to every individual based on HRV data measured over a long period of daily life.

BLOCK ENTROPY ANALYSIS OF HEART RATE VARIABILITY SIGNALS

In this paper we present a novel approach to the analysis of Heat Rate Variability (HRV) data, by coarse-graining analysis using the estimation of Block Entropies with the technique of lumping. HRV time series are generated from long recordings of Electrocardiograms (ECGs) and are then filtered in order to produce a coarse-grained symbolic dynamics. Block Entropy analysis is applied to these dynamics in order to examine its coarse-grained statistics. Our data set is comprised of two subsets, one of healthy subjects and another of Coronary Artery Disease (CAD) patients. It is found that Entropy analysis provides a quick and efficient tool for the differentiation of these series according to subject category. Healthy subjects provided more complex statistics compared to patients; specifically, the healthy data files provided higher values of block Entropies compared to patient ones. We also compare these results with the Correlation Dimension Estimation in order to establish coherency. We believe that this analysis may provide a useful statistical method towards the better understanding of the human cardiac system.

Evaluation of receiver operator characteristics — optimum time of day for the assessment of heart rate variability after acute myocardial infarction

The study reports a mathematical method for deciding which clinical data are of importance for treatment decisions in a given clinical setting. The method comprises the following steps: (A) the receiver operator characteristic (ROC) functions of the compared sets of data are computed; (B) the design and aim of the clinical study is expressed as an integral measure on the space of sensitivity values (this reflects the preference of low or high sensitivity values dependent on the clinical targets); (C) the sets of data being compared are characterised by the non-linear integrals of their ROC functions. The approach has been used to compare mean heart rate (HR) and heart rate variability (HRV) data calculated in 5113 different portions of 24-h ECG recordings and assessed in 365 patients surviving acute myocardial infarction, in order to evaluate the utility of Holter recording of varying lengths and starting times for the prediction of sudden cardiac death and/or serious arrhythmic events. The results of the study show that this approach is capable of evaluating and comparing the sets of medical data used for identification of patients who are at increased risk. The experimental part of the study showed that the optimum recording interval for the assessment of HR and HRV data in patients who survived acute myocardial infarction depends on the aim of the identification of increased risk patients. The optimum interval of recording is different for an identification which requires a low number of false negative cases and permits a higher number of false positive cases, than for the situation where a low number of false positive cases are required and a higher number of false negative cases are permissible.

The Development and Validation of an Analytical Training Program for Medical Suturing

As a result of experimental evidence on skills acquisition of industrial and dental tasks, the concept of utilizing analytical training methods in psychomotor medical education was investigated in this study on “everting square suture” technique used in plastic surgery procedures. Based on attitude questionnaires and film analysis, three simulation training models were developed and compared as to training effectiveness against the “traditional” training method (practice on a pig's foot) currently in use at a number of Medical Schools. The three proposed training models consisted of: 1. Electromechanical training simulator using the progressive parts training method. 2. Perceptual training through film viewing and fault analysis. 3. The combination of (1) and (2) above. Thirty-six medical students with no prior exposure to wound closure techniques participated as subjects. These students were randomly divided into four groups (three experimental and one control). Intergroup comparisons were attained by having subjects from the experimental groups, subsequent to training, practice on porcine skin by placing 35 sutures similar to that of the traditional group. Time, quality, heart rate variability, chin-EMG, neck-EMG and plantar skin conductance data were collected throughout the task and used as evaluation measures. Results indicate that: 1. All three experimental training methods provided for significantly better quality performance levels (from 14.2 to 177.1 percent better) than the traditional training group. Group 1 attained the highest results. 2. The subjects on the experimental traininggroup had briefer training time (ranging between 1.4 to 3.1 times less) than the “traditional” group. 3. The subjects in the experimental training groups had less psychophysiological stress during task performance than did the subjects in the conventional training group.