Multiscale entropy and detrended fluctuation analysis of QT interval and heart rate variability during normal pregnancy

Pregnancy leads to physiological changes in various parameters of the cardiovascular system. The aim of this study was to investigate longitudinal changes in the structure and complexity of heart rate variability (HRV) and QT interval variability during the second half of normal gestation. We analysed 30-min high-resolution ECGs recorded monthly in 32 pregnant women, starting from the 20th week of gestation. Heart rate and QT variability were quantified using multiscale entropy (MSE) and detrended fluctuation analyses (DFA). DFA of HRV showed significantly higher scaling exponents towards the end of gestation (p<0.0001). MSE analysis showed a significant decrease in sample entropy of HRV with progressing gestation on scales 1-4 (p<0.05). MSE analysis and DFA of QT interval time series revealed structures significantly different from those of HRV with no significant alteration during the second half of gestation. In conclusion, pregnancy is associated with increases in long-term correlations and regularity of HRV, but it does not affect QT variability. The structure of QT time series is significantly different from that of RR time series, despite its close physiological dependence.     

基于短时心率变异性信号两种熵测度的过速型室性心律失常的预测分析

目的:过速型室性心律失常［持续性室性心动过速或心室纤颤（VT/VF）］是心脏猝死的主要诱因,测试VT/VF发生前心率变异性信号是否有明显改变可作为VT和VF发生的提前预报信号。方法:以78名患者体内心脏复律除颤器记录的VT/VF事件发生前心率变异性信号（VT/VF序列）和来自同一患者的正常窦性节律（CON序列）组成的135个样本对作为实验序列。通过预处理消除实验序列的伪差、异位心搏等干扰,采用两种基于熵的非线性复杂度测度——样本熵和逐点多尺度熵（PPMSE）,分析VT和VF发生前十几分钟的VT/VF序列,以及心率增加和减小的VT/VF序列复杂性,并采用PPMSE方法讨论了接近VT/VF发生时VT/VF序列复杂性变化。结果:与正常对照组CON序列相比,在一定匹配容差内,VT/VF发生前心率变异性信号的样本熵明显减小（r<0.25×SD, P<0.000 5）,心率增加的VT/VF序列减小更显著（r<0.3×SD, P<0.000 1）;VT/VF序列的PPMSE在越接近VT/VF发生时刻减小越显著,提取的CI指数存在显著差异（如1~30尺度,N=986、500、250时,P=1.5×10-2、P=4.3×10-3、P=1.3×10-5）,心率增加的VT/VF序列区分性能更好。结论:过速型心律失常的自然发作并不是突发现象,在其发作前或许存在某种生理预兆,两种熵测度可能是短时预报恶性室性心律失常事件的有效非线性参数。     

Applying fractal analysis to heart rate time series of sheep experiencing pain

The objective assessment of pain is difficult in animals and humans alike. Detrended fluctuation analysis (DFA) is a method which extracts “hidden” information from heart rate time series, and may offer a novel way of assessing the subjective experience associated with pain. The aim of this study was to investigate whether any fractal differences could be detected in heart rate time series of sheep due to the infliction of ischaemic pain. Heart rate variability (HRV) was recorded continuously in five ewes during treatment sequences of baseline, intervention and post-intervention for up to 60 min. Heart rate time series were subjected to a DFA, and the median of the scaling coefficients (α) was found to be α = 1.10 for the baseline sequences, 1.01 for the intervention sequences and 1.00 for the post-intervention sequences. The complexity in the regulation of heartbeats decreased between baseline and intervention (p ∼ 0.03) and baseline and post-intervention (p ∼ 0.01), indicating reperfusion pain and nociceptive sensitization in the post-intervention sequence. Random time series based on Gaussian white noise were generated, with similar mean and variance to the HRV sequences. No difference was found between these series (p ∼ 0.28), pointing to a true difference in complexity in the original data. We found no difference in the scaling coefficient α between the different treatments, possibly due to the small sample size or a fear induced sympathetic arousal during test day 1 confounding the results. The decrease in the scaling coefficient α may be due to sympathetic activation and vagal withdrawal. DFA of heart rate time series may be a useful method to evaluate the progressive shift of cardiac regulation toward sympathetic activation and vagal withdrawal produced by pain or negative emotional responses such as fear.     

Analysis of heart rate variability signal during meditation using deterministic-chaotic quantifiers

Abstract

This study investigated the level of chaos and the existence of fractal patterns in the heart rate variability (HRV) signal prior to meditation and during meditation using two quantifiers adapted from non-linear dynamics and deterministic chaos theory: (1) component central tendency measures (CCTMs) and (2) Higuchi fractal dimension (HFD). CCTM quantifies degree of variability/chaos in the specified quadrant of the second-order difference plot for HRV time series, while HFD quantifies dimensional complexity of the HRV series. Both the quantifiers yielded excellent results in discriminating the different psychophysiological states. The study found (1) significantly higher first quadrant CCTM values and (2) significantly lower HFD values during meditation state compared to pre-meditation state. Both of these can be attributed to the respiratory-modulated oscillations shifting to the lower frequency region by parasympathetic tone during meditation. It is thought that these quantifiers are most promising in providing new insight into the evolution of complexity of underlying dynamics in different physiological states.     

Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography

Heart rate variability (HRV) is traditionally derived from RR interval time series of electrocardiography (ECG). Photoplethysmography (PPG) also reflects the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Thus, theoretically, PPG can be used for determining the interval between successive heartbeats and heart rate variability. However, the PPG wave lags behind the ECG signal by the time required for transmission of pulse wave. In this study, finger-tip PPG and standard lead II ECG were recorded for five minutes from 10 healthy subjects at rest. The results showed a high correlation (median = 0.97) between the ECG-derived RR intervals and PPG-derived peak-to-peak (PP) intervals. PP variability was accurate (0.1 ms) as compared to RR variability. The time domain, frequency domain and Poincaré plot HRV parameters computed using RR interval method and PP interval method showed no significant differences (p < 0.05). The error analysis also showed insignificant differences between the HRV indices obtained by the two methods. Bland-Altman analysis showed high degree of agreement between the two methods for all the parameters of HRV. Thus, HRV can also be reliably estimated from the PPG based PP interval method.     

Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography

Heart rate variability (HRV) is traditionally derived from RR interval time series of electrocardiography (ECG). Photoplethysmography (PPG) also reflects the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Thus, theoretically, PPG can be used for determining the interval between successive heartbeats and heart rate variability. However, the PPG wave lags behind the ECG signal by the time required for transmission of pulse wave. In this study, finger-tip PPG and standard lead II ECG were recorded for five minutes from 10 healthy subjects at rest. The results showed a high correlation (median = 0.97) between the ECG-derived RR intervals and PPG-derived peak-to-peak (PP) intervals. PP variability was accurate (0.1 ms) as compared to RR variability. The time domain, frequency domain and Poincaré plot HRV parameters computed using RR interval method and PP interval method showed no significant differences (p < 0.05). The error analysis also showed insignificant differences between the HRV indices obtained by the two methods. Bland-Altman analysis showed high degree of agreement between the two methods for all the parameters of HRV. Thus, HRV can also be reliably estimated from the PPG based PP interval method.     

Nonlinear heart rate dynamics: circadian profile and influence of age and gender

Heart rate variability (HRV) is used as a marker of autonomic modulation of heart rate. Nonlinear HRV parameters providing information about the scaling behaviour or the complexity of the cardiac system were included. In addition, the chaotic behaviour was quantified by means of the recently developed numerical noise titration technique. 24h Holter recordings of a large healthy population (N=276, 141 males, 18-71 years of age) were available. The goal was to investigate the influence of gender, age and day-night variation on these nonlinear HRV parameters. Numerical titration yielded similar information as other nonlinear HRV parameters do. However, it does not require long and cleaned data and therefore applicable on short (5min) noisy time series. A higher nonlinear behaviour was observed during the night (NLdr; day: 50.8±19.6%, night: 59.1±19.5%; P<0.001) while nonlinear heart rate fluctuations decline with increasing age (NLdr; Pearson correlation coefficient r between -0.260 and -0.319 dependent on gender and day or night, all P<0.01). A clear circadian profile could be found for almost every parameter, showing in particular which changes occur during the transition phases of waking up and going to sleep. Our results support the involvement of the autonomic nervous system in the generation of nonlinear and complex heart rate dynamics.     

Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators

ObjectiveThe paper presents a diagnostic algorithm for classifying cardiac tachyarrhythmias for implantable cardioverter defibrillators (ICDs). The main aim was to develop an algorithm that could reduce the rate of occurrence of inappropriate therapies, which are often observed in existing ICDs. To achieve low energy consumption, which is a critical factor for implantable medical devices, very low computational complexity of the algorithm was crucial. The study describes and validates such an algorithm and estimates its clinical value.MethodologyThe algorithm was based on the heart rate variability (HRV) analysis. The input data for our algorithm were: RR-interval (I), as extracted from raw intracardiac electrogram (EGM), and in addition two other features of HRV called here onset (ONS) and instability (INST). 6 diagnostic categories were considered: ventricular fibrillation (VF), ventricular tachycardia (VT), sinus tachycardia (ST), detection artifacts and irregularities (including extrasystoles) (DAI), atrial tachyarrhythmias (ATF) and no tachycardia (i.e. normal sinus rhythm) (NT). The initial set of fuzzy rules based on the distributions of I, ONS and INST in the 6 categories was optimized by means of a software tool for automatic rule assessment using simulated annealing. A training data set with 74 EGM recordings was used during optimization, and the algorithm was validated with a validation data set with 58 EGM recordings. Real life recordings stored in defibrillator memories were used. Additionally the algorithm was tested on 2 sets of recordings from the PhysioBank databases: MIT-BIH Arrhythmia Database and MIT-BIH Supraventricular Arrhythmia Database. A custom CMOS integrated circuit implementing the diagnostic algorithm was designed in order to estimate the power consumption. A dedicated Web site, which provides public online access to the algorithm, has been created and is available for testing it.ResultsThe total number of events in our training and validation sets was 132. In total 57 shocks and 28 antitachycardia pacing (ATP) therapies were delivered by ICDs. 25 out of 57 shocks were unjustified: 7 for ST, 12 for DAI, 6 for ATF. Our fuzzy rule-based diagnostic algorithm correctly recognized all episodes of VF and VT, except for one case where VT was recognized as VF. In four cases short lasting, spontaneously ending VT episodes were not detected (in these cases no therapy was needed and they were not detected by ICDs either). In other words, a fuzzy logic algorithm driven ICD would deliver one unjustified shock and deliver correct therapies in all other cases. In the tests, no adjustments of our algorithm to individual patients were needed. The sensitivity and specificity calculated from the results were 100% and 98%, respectively. In 126 ECG recordings from PhysioBank (about 30 min each) our algorithm incorrectly detected 4 episodes of VT, which should rather be classified as fast supraventricular tachycardias. The estimated power consumption of the dedicated integrated circuit implementing the algorithm was below 120 nW.ConclusionThe paper presents a fuzzy logic-based control algorithm for ICD. Its main advantages are: simplicity and ability to decrease the rate of occurrence of inappropriate therapies. The algorithm can work in real time (i.e. update the diagnosis after every RR-interval) with very limited computational resources.     

Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition

How to quantify the complexity of a physiological signal is a crucial issue for verifying the underlying mechanism of a physiological system. The original algorithm of detrended fluctuation analysis (DFA) quantifies the complexity of signals using the DFA scaling exponent. However, the DFA scaling exponent is suitable only for an integrated time series but not the original signal. Moreover, the method of least squares line is a simple detrending operation. Thus, the analysis results of the original DFA are not sufficient to verify the underlying mechanism of physiological signals. In this study, we apply an innovative timescale-adaptive algorithm of empirical mode decomposition (EMD) as the detrending operation for the modified DFA algorithm. We also propose a two-parameter scale of randomness for DFA to replace the DFA scaling exponent. Finally, we apply this modified algorithm to the database of human heartbeat interval from Physiobank, and it performs well in identifying characteristics of heartbeat interval caused by the effects of aging and of illness.     

Does preprocessing change nonlinear measures of heart rate variability?

This work investigated if methods used to produce a uniformly sampled heart rate variability (HRV) time series significantly change the deterministic signature underlying the dynamics of such signals and some nonlinear measures of HRV. Two methods of preprocessing were used: the convolution of inverse interval function values with a rectangular window and the cubic polynomial interpolation. The HRV time series were obtained from 33 Wistar rats submitted to autonomic blockade protocols and from 17 healthy adults. The analysis of determinism was carried out by the method of surrogate data sets and nonlinear autoregressive moving average modelling and prediction. The scaling exponents , ₁ and ₂ derived from the detrended fluctuation analysis were calculated from raw HRV time series and respective preprocessed signals. It was shown that the technique of cubic interpolation of HRV time series did not significantly change any nonlinear characteristic studied in this work, while the method of convolution only affected the ₁ index. The results suggested that preprocessed time series may be used to study HRV in the field of nonlinear dynamics.     

Relationships among frequency domain and non-linear parameters from heart rate variability

Frequency domain analysis of heart rate variability (HRV) is used for the evaluation of autonomic activity. Non-linear domain parameters from HRV are also considered useful. However, properties of the latter have not yet been clearly characterized. Therefore, we studied the relationships among the frequency domain and non-linear parameters from HRV. Continuous Holter electrocardiographic monitoring was conducted on 43 healthy female medical staff including laboratory technologists and nurses during an 8-hour working period in our hospital. Low and high frequency components (LF and HF, respectively) of the frequency domain, recurrence rate (REC%) on recurrence plot analysis, scaling exponents al and a2 on detrended fluctuation analysis, and approximate entropy (ApEn) were obtained from HRV. Both the LF/HF ratio and HF were correlated with al and ApEn. REC% was correlated with ApEn and alpha2, whereas alpha2 was correlated only with REC%. Although autonomic parameters from the frequency domain are closely related with some of the non-linear parameters, it is suggested that a2 and REC% reflect different physiological activities.     

短时间序列的心率变异性信号的关联维数计算

心率变异性信号的分析在临床诊断和生理研究中都有重要的应用价值,本文将关联维数的短时间序列算法应用于心率变异性,不仅提高了心率变异性信号的混沌动力学参数的计算的正确性,也扩大了分析的适用范围。     

Breathing frequency bias in fractal analysis of heart rate variability

Detrended Fluctuation Analysis (DFA) is an algorithm widely used to determine fractal long-range correlations in physiological signals. Its application to heart rate variability (HRV) has proven useful in distinguishing healthy subjects from patients with cardiovascular disease. In this study we examined the effect of respiratory sinus arrhythmia (RSA) on the performance of DFA applied to HRV. Predictions based on a mathematical model were compared with those obtained from a sample of 14 normal subjects at three breathing frequencies: 0.1 Hz, 0.2 Hz and 0.25 Hz. Results revealed that: (1) the periodical properties of RSA produce a change of the correlation exponent in HRV at a scale corresponding to the respiratory period, (2) the short-term DFA exponent is significantly reduced when breathing frequency rises from 0.1 Hz to 0.2 Hz. These findings raise important methodological questions regarding the application of fractal measures to short-term HRV.     

Heart Rate Variability and Length of Survival in Hospice Cancer Patients

We examined the association between heart rate variability (HRV) and survival duration to evaluate the usefulness of HRV as a prognostic factor for hospice cancer patients. In terminally ill cancer patients who visited the Hospice clinic, we checked demographic data, Karnofsky performance scale (KPS), HRV, dyspnea, anorexia, as well as fasting blood glucose and total cholesterol. After following up their duration of survival, we examined meaningful prognostic factors for predicting life expectancy through the survival analysis. A total of 68 patients were included in final analysis. As KPS was lower, or when combined with dyspnea or anorexia, the survival duration was much shorter. HRV parameters except heart rate were all impaired in most patients. In particular, the group with mean heart rate of 100 or more beats per minute and the group with standard deviations of normal-to-normal R-R intervals (SDNN) of 21.3 ms (75 percentile) or less showed significantly shorter survival duration. The final multivariate analysis adjusting for age, gender, fasting blood glucose, and total cholesterol showed that KPS, dyspnea, anorexia, and SDNN were significant prognostic factors in survival duration. For the first time, we report that SDNN is a prognostic factor in terminal cancer patients.     

Instantaneous changes in heart rate regulation due to mental load in simulated office work

The cardiac regulation effects of a mental task added to regular office work are described. More insight into the time evolution during the different tasks is created by using time–frequency analysis (TFA). Continuous wavelet transformation was applied to create time series of instantaneous power and frequency in specified frequency bands (LF 0.04–0.15 Hz; HF 0.15–0.4 Hz), in addition to the traditional linear heart rate variability (HRV) parameters. In a laboratory environment, 43 subjects underwent a protocol with three active conditions: a clicking task with low mental load and a clicking task with high mental load (mental arithmetic) performed twice, each followed by a rest condition. The heart rate and measures related to vagal modulation could differentiate the active conditions from the rest condition, meaning that HRV is sensitive to any change in mental or physical state. Differences between physical and mental stress were observed and a higher load in the combined task was observed. Mental stress decreased HF power and caused a shift toward a higher instantaneous frequency in the HF band. TFA revealed habituation to the mental load within the task (after 3 min) and between the two tasks with mental load. In conclusion, the use of TFA in this type of analysis is important as it reveals extra information. The addition of a mental load to a physical task elicited further effect on HRV parameters related to autonomic cardiac modulation.     

Effect of mobile phone radiation on heart rate variability

The rapid increase in the use of mobile phones (MPs) in recent years has raised the problem of health risk connected with high-frequency electromagnetic fields. There are reports of headache, dizziness, numbness in the thigh, and heaviness in the chest among MP users. This paper deals with the neurological effect of electromagnetic fields radiated from MPs, by studies on heart rate variability (HRV) of 14 male volunteers. As heart rate is modulated by the autonomic nervous system, study of HRV can be used for assessing the neurological effect. The parameters used in this study for quantifying the effect on HRV are scaling exponent and sample entropy. The result indicates an increase in both the parameters when MP is kept close to the chest and a decrease when kept close to the head. MP has caused changes in HRV indices and the change varied with its position, but the changes cannot be considered significant as the p values are high.     

睡眠生理参数的去趋势波动分析

去趋势波动分析(DFA)适宜于研究各类非稳态时间序列的长程幂函数相关性。我们采用DFA方法分析脑电、心电RR间期序列和搏出量等睡眠生理参数,计算定标指数α,研究各睡眠阶段的特点。实验结果显示,各睡眠阶段的α值具有明显的差异,脑电和搏出量信号的规律相似,α随睡眠加深而增大,而RR间期序列的规律则相反,α随睡眠加深而减小。表明DFA在生理参数分析中具有良好的应用价值。     

Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure

In this study, best combination of short-term heart rate variability (HRV) measures are sought for to distinguish 29 patients with congestive heart failure (CHF) from 54 healthy subjects in the control group. In the analysis performed, in addition to the standard HRV measures, wavelet entropy measures are also used. A genetic algorithm is used to select the best ones from among all possible combinations of these measures. A k-nearest neighbor classifier is used to evaluate the performance of the feature combinations in classifying these two groups. The results imply that two combinations of all HRV measures, both of which include wavelet entropy measures, have the highest discrimination power in terms of sensitivity and specificity values.     

以短时心率变异性反映充血性心力衰竭患者自主神经活动的改变

基于短时心率变异性（HRV）分析,探讨充血性心力衰竭（CHF）患者自主神经活动的变化和影响。选用THEW数据库中正常人子数据库作为正常对照组（n=189）,对于PhysioNet中两个CHF子数据库的样本（n=44）,按照NYHA等级,将NYHA I-II级划分轻度CHF 组（n=12）,NYHA III-IV级为重度CHF组（n=32）。对每一个Holter记录选取日间和夜间安静态各5 min的RR间期（RRI）序列,分别进行时域、基于AR模型的频域和去趋势波动（DFA）分析。在正常组、轻度CHF组和重度CHF组等三组中,CHF患者日间的短时分形尺度指数（(α₁)_d）两两比较均有显著性差异,并存在下降趋势（依次分别为1.35±0.21、1.03±0.29和0.81±0.29）,反映心率动力学从分形特性转向更随机化的结构。同时,日间HFn((HFn)_d)在三组间的两两比较中均存在显著性差异,并存在上升趋势（依次分别为23.89%±12.78%、37.22%±11.24%和56.30%±15.28%）, 表明CHF导致交感神经和迷走神经交互作用趋于消失。利用夜间RRI（RRI_n）,(HFn)_d 和 (α₁)_d等3个指标进行Fisher线性判别,区分正常人和CHF患者的灵敏性和特异性分别为90.91%和92.06%,而区分轻度和重度CHF患者的灵敏性和特异性分别为84.38%和100%。所进行的研究将HRV非线性方法与传统方法相结合评估自主神经状态, 为监测CHF病情或观察治疗效果等潜在的临床应用提供了依据。     

Automatic assessment of the interaction between respiration and heart rate variability signal

The present paper introduces an original method of processing heart rate variability (HRV) and respiration signals as detected respectively through chest electrodes and thoracic belt in dogs under different experimental conditions. Signals are processed as time series synchronous with the occurrence of QRS complexes on ECG signal and auto and cross spectra are accordingly calculated. Two particular bands appear mainly of interest on the spectrum of HRV signal: one in correspondence with the respiration rate and another one at a lower frequency value. Values of power at these frequency bands together with coherence and phase between HRV signal and respiration complete the parameters which try to quantify a few aspects of the complex dynamic relationships between the original signals. In particular, controlled respiration in dogs was studied through the connection with an automatic ventilator, as well as the effects of drugs which interact with the neural regulatory systems (i.e. sympathetic and parasympathetic nervous system). Gain and phase relationships between heart rate variability and respiration, obtained with spectral analysis, could be used to provide a better understanding of the neural control mechanisms linking heart rate and respiration in various experimental conditions. The method described in this study is to be used both in physiological and clinical research.