Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.     

Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal

OBJECTIVE: This paper presents an effective cardiac arrhythmia classification algorithm using the heart rate variability (HRV) signal. The proposed algorithm is based on the generalized discriminant analysis (GDA) feature reduction scheme and the support vector machine (SVM) classifier. METHODOLOGY: Initially 15 different features are extracted from the input HRV signal by means of linear and nonlinear methods. These features are then reduced to only five features by the GDA technique. This not only reduces the number of the input features but also increases the classification accuracy by selecting most discriminating features. Finally, the SVM combined with the one-against-all strategy is used to classify the HRV signals. RESULTS: The proposed GDA- and SVM-based cardiac arrhythmia classification algorithm is applied to input HRV signals, obtained from the MIT-BIH arrhythmia database, to discriminate six different types of cardiac arrhythmia. In particular, the HRV signals representing the six different types of arrhythmia classes including normal sinus rhythm, premature ventricular contraction, atrial fibrillation, sick sinus syndrome, ventricular fibrillation and 2 degrees heart block are classified with an accuracy of 98.94%, 98.96%, 98.53%, 98.51%, 100% and 100%, respectively, which are better than any other previously reported results. CONCLUSION: An effective cardiac arrhythmia classification algorithm is presented. A main advantage of the proposed algorithm, compared to the approaches which use the ECG signal itself is the fact that it is completely based on the HRV (R-R interval) signal which can be extracted from even a very noisy ECG signal with a relatively high accuracy. Moreover, the usage of the HRV signal leads to an effective reduction of the processing time, which provides an online arrhythmia classification system. A main drawback of the proposed algorithm is however that some arrhythmia types such as left bundle branch block and right bundle branch block beats cannot be detected using only the features extracted from the HRV signal.     

Heart rate variability: a review

Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random—during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.     

smRithm: Graphical user interface for heart rate variability analysis

Abstract

Over the past 25 years, Heart rate variability (HRV) has become a non-invasive research and clinical tool for indirectly carrying out investigation of both cardiac and autonomic system function in both healthy and diseased. It provides valuable information about a wide range of cardiovascular disorders, pulmonary diseases, neurological diseases, etc. Its primary purpose is to access the functioning of the nervous system. The source of information for HRV analysis is the continuous beat to beat measurement of inter-beat intervals. The electrocardiography (ECG or EKG) is considered as the best way to measure inter-beat intervals. This paper proposes an open source Graphical User Interface (GUI): smRithm developed in MATLAB for HRV analysis that will apply effective techniques on the raw ECG signals to process and decompose it in a simpler manner to obtain more useful information out of signals that can be utilized for more powerful and efficient applications in the near future related to HRV.     

基于多分辨率分析的心率变异性分析

目的：心率变异性蕴藏了大量有关心血管调节的信息,可作为定量反映自主神经功能及其对心血管的调控作用和反映心脏活动正常与否的重要指标之一。因此,主要研究了几种常用的心率变异性分析方法。方法：本论文应用多分辨率分析方法对HRV信号进行6尺度分解,计算小波系数的能量及其在各频段的分布,对cd3-cd6频段的能量进行分析比较。结果：充血性心衰患者各频段能量均值比正常人都要低,能量分别主要集中在高频段和低频段的偏低频部分,且两组的能量差异更为明显,与频域分析结果相比更为精确。结论：要得到带有普遍规律的实验结果,需要对不同的分析方法进行不断地改进和优化,并进行大量的研究工作。     

脑缺血缺氧HRV信号的复原图分析

心率变异信号(HRV)是人体心脏搏动周期的微小变异,反映了自主神经系统的平衡协调关系.在证实心率变异信号的非线性特征后,可利用基于复杂度的非线性动力学分析方法-复原图(recurrence plot)来进行分析.通过复原图及其量化分析方法,发现与正常情况相比,缺血缺氧阶段HRV信号的复原图和量化指标L-Mean、L-Entr均有显著的变化,为监测缺血缺氧脑损伤提供了新的途径.     

Changes in resting heart rate variability across the menstrual cycle

Heart rate variability (HRV) is a noninvasive indicator of autonomic control. This study examines HRV changes across a normal menstrual cycle and proposes a novel piecewise function controlling for the effects of breathing on HRV spectral parameters. A resting ECG was collected from 13 women at five points in their menstrual cycle. Both heart rate and breathing rate increased across the cycle (p < .01) while time‐domain variability decreased (p = .04). Use of the piecewise function for breathing rate in HRV spectral analysis was confirmed by a substantial increase in model goodness‐of‐fit. HRV spectral parameters, controlled for breathing with the piecewise function, confirm that the decrease in variability is likely due to a parasympathetic withdrawal, since high frequency HRV decreases (p = .02).     

Analysis of cardiac health using fractal dimension and wavelet transformation

Analysis of heart rate variation (HRV) has become a popular non-invasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. This paper presents the continuous time wavelet analysis of heart rate variability signal for disease identification. Fractal dimension (FD) of heart rate signals are calculated and compared with the wavelet analysis patterns. The FD obtained indicates more than 90% confidence interval for all the classes studied.     

Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features

Objective

The paper addresses a common and recurring problem of electrocardiogram (ECG) classification based on heart rate variability (HRV) analysis. Current understanding of the limits of HRV analysis in diagnosing different cardiac conditions is not complete. Existing research suggests that a combination of carefully selected linear and nonlinear HRV features should significantly improve the accuracy for both binary and multiclass classification problems. The primary goal of this work is to evaluate a proposed combination of HRV features. Other explored objectives are the comparison of different machine learning algorithms in the HRV analysis and the inspection of the most suitable period T between two consecutively analyzed R-R intervals for nonlinear features.

Methods and material

We extracted 11 features from 5 min of R-R interval recordings: SDNN, RMSSD, pNN20, HRV triangular index (HTI), spatial filling index (SFI), correlation dimension, central tendency measure (CTM), and four approximate entropy features (ApEn1-ApEn4). Analyzed heart conditions included normal heart rhythm, arrhythmia (any), supraventricular arrhythmia, and congestive heart failure. One hundred patient records from six online databases were analyzed, 25 for each condition. Feature vectors were extracted by a platform designed for this purpose, named ECG Chaos Extractor. The vectors were then analyzed by seven clustering and classification algorithms in the Weka system: K-means, expectation maximization (EM), C4.5 decision tree, Bayesian network, artificial neural network (ANN), support vector machines (SVM) and random forest (RF). Four-class and two-class (normal vs. abnormal) classification was performed. Relevance of particular features was evaluated using 1-Rule and C4.5 decision tree in the cases of individual features classification and classification with features’ pairs.

Results

Average total classification accuracy obtained for top three classification methods in the two classes’ case was: RF 99.7%, ANN 99.1%, SVM 98.9%. In the four classes’ case the best results were: RF 99.6%, Bayesian network 99.4%, SVM 98.4%. The best overall method was RF. C4.5 decision tree was successful in the construction of useful classification rules for the two classes’ case. EM and K-means showed comparable clustering results: around 50% for the four classes’ case and around 75% for the two classes’ case. HTI, pNN20, RMSSD, ApEn3, ApEn4 and SFI were shown to be the most relevant features. HTI in particular appears in most of the top-ranked pairs of features and is the best analyzed feature. The choice of the period T for nonlinear features was shown to be arbitrary. However, a combination of five different periods significantly improved classification accuracy, from 70% for a single period up to 99% for five periods.

Conclusions

Analysis shows that the proposed combination of 11 linear and nonlinear HRV features gives high classification accuracy when nonlinear features are extracted for five periods. The features’ combination was thoroughly analyzed using several machine learning algorithms. In particular, RF algorithm proved to be highly efficient and accurate in both binary and multiclass classification of HRV records. Interpretable and useful rules were obtained with C4.5 decision tree. Further work in this area should elucidate which features should be extracted for the best classification results for specific types of cardiac disorders.     

Myocardial determinants in regulation of the heart rate

Heart rate is a function of at least three factors located in the sinus node, including the pacemaker and the activity of the sympathetic and vagal pathways. Heart rate varies during breathing and exercising. The is far from being a purely academic question because, after myocardial infarction or in cardiac insufficiency, reduced heart rate variability (HRV) represents the most valuable prognostic factor. HRV is usually considered index of the sympathovagal balance and is explored using time domain analysis, such as spectral analysis. Nevertheless, methods such as the Fast Fourier Transformation are not applicable to small rodents which have an unstable heart rate with asymmetric oscillations. Nonlinear methods show chaotic behavior under some conditions. A time and frequency domain method of analysis, the Wigner-Villé Transform, has been proposed for the study of HRV in both humans and small rodents, as a compromise between linear and nonlinear methods. We developed a method to quantify both arrhythmias and HRV in unanesthetized rodents. Such a method allows study of the relationship between the physiological parameters and the myocardial phenotype. Ventricular premature beats are more frequent in 16-month-old spontaneously hypertensive rats than in age-matched controls. In addition, HRV is attenuated in spontaneously hypertensive rats, as in compensatory cardiac hypertrophy in humans, and such attenuation is considered a prognostic index. Converting enzyme inhibition reduces in parallel arterial hypertension, cardiac hypertrophy, and ventricular fibrosis; it prevents ventricular premature beats and normalizes heart rate variability. It can be demonstrated that the incidence of ventricular premature beats is linked to the myocardial phenotype in terms of both cardiac hypertrophy and fibrosis. The two factors act as independent variables. HRV is correlated with the incidence of arrhythmias, suggesting that the beneficial effects of converting enzyme inhibition are related to prevention of arrhythmias.     

Nonlinear analysis of heart rate variability of healthy subjects and patients with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is one of the causes of mortality worldwide with an increasing prevalence. Heart rate variability (HRV) reflects the regulation mechanism of the cardiac activity by the autonomic nervous system. The assessment of HRV by using nonlinear methods is more sensitive for the detection of complexity when compared to linear methods. This study aims to get information about the autonomic dysfunction occurred in patients with COPD by analysing the complexity of HRV. Electrocardiogram signals recorded from healthy subjects, patients with moderate COPD and severe COPD (eight subjects per group) were analysed. The HRV signals were acquired from ECG signals. Signals were reconstructed in the phase space and largest Lyapunov exponent (LLE), correlation dimension, Hurst exponent and approximate entropy (ApEn) values were calculated. It has seen that for the patients with COPD LLE, correlation dimension, Hurst exponent and ApEn values were less than control group. According to this, HRV complexity decreases in the presence of COPD. However, there is no significant difference between COPD groups and the severity of COPD has no effect on the chaoticity of the system. The results revealed that autonomic dysfunction occurred in patients with COPD is associated with reduced HRV complexity.     

基于心率变异性分析的睡眠分期算法研究和验证

为实现睡眠分期,为穿戴式生理参数监测技术在慢病监测领域的应用提供技术支撑,发展基于心率变异性和支持向量机模型的睡眠分期算法。从心率时间间期序列中提取时域、频域和非线性等86个特征,将多导睡眠图仪的三分类结果(醒、快速眼动期、非快速眼动期)作为“金标准”,采用支持向量机作为多分类器模型;为保证训练集数据质量,使用开放睡眠数据库SHHS中由专家确认挑选的67例PSG样本作为训练集,实现特征筛选和模型参数训练。为验证模型的泛化性能,从SHHS数据库中进一步随机提取939例PSG样本,对模型性能进行测试。睡眠分期模型在训练集上的五折交叉验证的准确率为84.00%±1.33%,卡帕系数为0.70±0.03;在939例测试集上的准确率为76.10%±10.80%,卡帕系数为0.57±0.15。剔除RR间期异常(110例)和明显睡眠结构异常(29例)的样本后,测试集(800例)的准确率为82.00%±5.60%,卡帕系数为0.67±0.14。所提出的基于心率变异性分析的睡眠分期算法具有较高的准确性,大样本人群测试结果表明,该模型具有较好的普适性。     

A preprocessing tool for removing artifact from cardiac RR interval recordings using three‐dimensional spatial distribution mapping

Artifact is common in cardiac RR interval data that is recorded for heart rate variability (HRV) analysis. A novel algorithm for artifact detection and interpolation in RR interval data is described. It is based on spatial distribution mapping of RR interval magnitude and relationships to adjacent values in three dimensions. The characteristics of normal physiological RR intervals and artifact intervals were established using 24‐h recordings from 20 technician‐assessed human cardiac recordings. The algorithm was incorporated into a preprocessing tool and validated using 30 artificial RR (ARR) interval data files, to which known quantities of artifact (0.5%, 1%, 2%, 3%, 5%, 7%, 10%) were added. The impact of preprocessing ARR files with 1% added artifact was also assessed using 10 time domain and frequency domain HRV metrics. The preprocessing tool was also used to preprocess 69 24‐h human cardiac recordings. The tool was able to remove artifact from technician‐assessed human cardiac recordings (sensitivity 0.84, SD = 0.09, specificity of 1.00, SD = 0.01) and artificial data files. The removal of artifact had a low impact on time domain and frequency domain HRV metrics (ranging from 0% to 2.5% change in values). This novel preprocessing tool can be used with human 24‐h cardiac recordings to remove artifact while minimally affecting physiological data and therefore having a low impact on HRV measures of that data.     

Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry

Heart rate variability refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability is important because it provides a window to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Parameters are extracted from the heart rate signals and analysed using computers for diagnostics. This paper describes the analysis of normal and seven types of cardiac abnormal signals using approximate entropy (ApEn), sample entropy (SampEn), recurrence plots and Poincare plot patterns. Ranges of these parameters for various cardiac abnormalities are presented with an accuracy of more than 95%. Among the two entropies, ApEn showed better performance for all the cardiac abnormalities. Typical Poincare and recurrence plots are shown for various cardiac abnormalities.     

Spectral and wavelet based assessment of congestive heart failure patients

Heart rate variability (HRV) can be quantified, among others, in the spectral and wavelet domain. The wavelet transform (WT) is an alternative method for the analysis of non-stationary signals. Some recent work shows that the scale-dependent WT standard deviation of the R-R intervals of human ECG can be used to distinguish patients with certain forms of cardiac pathological function from normal subjects. In this paper, we show an explicit relationship between variance of WT and corresponding spectral measure. Also, the statistics of the estimator for variance of WT is obtained. Numerical simulations support the theoretical results. By comparing expected value and variance and spectral measures, we conclude that WT measures are able to diagnose certain cardiac system function.     

Multiscale Analysis of Heart Rate Variability: A Comparison of Different Complexity Measures

Heart rate variability (HRV) is an important dynamical variable of the cardiovascular function. There have been numerous efforts to determine whether HRV dynamics are chaotic or random, and whether certain complexity measures are capable of distinguishing healthy subjects from patients with certain cardiac disease. In this study, we employ a new multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize the relative importance of nonlinear, chaotic, and stochastic dynamics in HRV of healthy, congestive heart failure (CHF), and atrial fibrillation subjects. We show that while HRV data of all these three types are mostly stochastic, the stochasticity is different among the three groups. Furthermore, we show that for the purpose of distinguishing healthy subjects from patients with CHF, features derived from SDLE are more effective than other complexity measures such as the Hurst parameter, the sample entropy, and the multiscale entropy.     

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.     

冠心病患者心率变异性分析

目的研究冠心病患者心率变异（HRV）的变化规律及临床意义。方法选择50例无心律失常冠心病患者（冠心病组）、30例伴心律失常冠心病患者（心率失常组）与52例正常成人自愿者（正常组）进行24h动态心电图HRV指标比较研究。结果与正常组比较,冠心病患者SDNN、SDANN、RMSSD、PNN50和HF指标均降低,LF指标升高,具有显著差异。伴心律失常与无心律失常冠心病患者比较,HRV指标异常变化趋于恶化。结论冠心病患者心脏自主神经调节功能受到损害,迷走神经活性减弱,交感神经活动占优势。