Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals

Heart rate variability is concerned with the analysis of the fluctuations in the interval between heart beats known as RR intervals. The long time RR time series obtained suffer from non-stationarity and the presence of ectopic beats, which prevents extraction of useful statistical information. The paper describes a wavelet-based technique for trend removal and a nonlinear filter to remove ectopic beats. This attempts to correct the limitations observed in a recent advanced heart rate toolkit [J. Niskanen, M.P. Tarvainen, P.O. Ranta-aho P.A. Karjalainen, Software for advanced HRV analysis, Comput. Meth. Prog. Biomed.,76 (2004) 73-81] when preprocessing. The results are encouraging. The preprocessed data are then used to obtain the standard deviation of RR interval time series (SDRR) of 15 healthy patients and 15 patients suffering from congestive heart failure. The results demonstrate the importance of preprocessing. The analysis show that the SDRR values of congestive heart failure patients are depressed compared to the healthy group.     

Fighter pilots’ heart rate,heart rate variation and performance during instrument approaches

Fighter pilots’ heart rate (HR), heart rate variation (HRV) and performance during instrument approaches were examined. The subjects were required to fly instrument approaches in a high-fidelity simulator under various levels of task demand. The task demand was manipulated by increasing the load on the subjects by reducing the range at which they commenced the approach. HR and the time domain components of HRV were used as measures of pilot mental workload (PMWL). The findings of this study indicate that HR and HRV are sensitive to varying task demands. HR and HRV were able to distinguish the level of PMWL after which the subjects were no longer able to cope with the increasing task demands and their instrument landing system performance fell to a sub-standard level. The major finding was the HR/HRV’s ability to differentiate the sub-standard performance approaches from the high-performance approaches.

Practitioner Summary:

This paper examined if HR and HRV were sensitive to varying task demands in a fighter aviation environment and if these measures were related to variations in pilot’s performance.     

Spectral analysis of heart rate variability as an indicator of driver fatigue

The effect of distance driven on three physiological variables taken to be indicators of fatigue was investigated on a 340 km highway circuit with eight inexperienced drivers as subjects. The physiological variables used were spectral values of heart rate variability in the 0.05-0.15Hz region (0.1 Hz HRV) supplemented by standard deviation of heart rate mean (S.D. HRV) and heart rate mean (HR). The analysis showed a significant relationship between 0.1 Hz HRV and distance driven while S.D. HRV and HR showed no direct relationship. The reason for this differential effect on the three physiological variables was discussed, and it was concluded that 0.1 Hz HRV seems to be a sensitive indicator of driver fatigue.     

基于光体积描述记成像技术的非接触低成本生理信息的检测

在最近几十年,人们对低成本、非接触和普适方法测量生理信息(心率、心率变异性、血氧饱和度等)产生了浓厚的兴趣。传统的临床测量生理信息的方法包括Ag/AgCl电极测量心率和心率变异性,二氧化碳分析仪测量呼吸率状态,脉搏血氧饱和度仪测量血氧饱和度。这些方法虽然可以获得完美的信号,但是他们价格昂贵、使用麻烦、不方便。基于光体积描述记成像技术检测生理信息提供了一个人体生理健康检测的新方法。血流速度、血流量和血压可间接地评估血容量,反过来,血容量间接反映了这些生理参数的变换。光在人体组织的反射或透射可以得到血容量的变化。使用电脑摄像头或手机摄像头扑捉的人体皮肤表面成像,通过对成像光信号的处理和分析,获得一些生理信息,如心率、呼吸率、心率变异性和血氧饱和度等。在本文中,我们回顾使用光体积描述记成像技术在非接触健康检测领域里的最新发展,论述面临的挑战和将来的发展方向。     

Robust heart rate variability analysis by generalized entropy minimization

Typical heart rate variability (HRV) times series are cluttered with outliers generated by measurement errors, artifacts and ectopic beats. Robust estimation is an important tool in HRV analysis, since it allows clinicians to detect arrhythmia and other anomalous patterns by reducing the impact of outliers. A robust estimator for a flexible class of time series models is proposed and its empirical performance in the context of HRV data analysis is studied. The methodology entails the minimization of a pseudo-likelihood criterion function based on a generalized measure of information. The resulting estimating functions are typically re-descending, which enable reliable detection of anomalous HRV patterns and stable estimates in the presence of outliers. The infinitesimal robustness and the stability properties of the new method are illustrated through numerical simulations and two case studies from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital data, an important benchmark data set in HRV analysis.     

Preprocessing effects in time–frequency distributions and spectral analysis of heart rate variability

Heart rate variability (HRV) is very significance noninvasive tool for autonomic nervous system (ANS) analysis. HRV signal includes both slowly changing components and rapidly changing transient events. This study presents effects of preprocessing of HRV in time–frequency analysis and spectral estimations. Preprocessing includes two levels as detrending of trend using smoothness prior method and correction of ectopics using integral pulse frequency modulation (IPFM). The datasets used in this study are obtained from the Spontaneous Ventricular Tachyarrhythmia (VTA) database. Datasets include least one ventricular tachyarrhythmia (VT) or ventricular fibrillation (VF) episode. Effects of preprocessing are investigated for time–frequency analysis using continuous wavelet transform (CWT) and spectrogram and for spectral analysis using periodogram, Welch's periodogram and Burg's periodogram. Performance of these methods in determination of VT or VF episode is analyzed. Importance of preprocessing is explained comparing of obtained results.     

Heart rate variability in relation to psychological factors

Fluctuations in heart rate are related to both physiological and psychological factors. A possible link between those factors has been investigated by examining heart rate variability (HRV)

In two groups of subjects characterized by a large difference in psychic state, psychiatric patients and normals, an investigation was conducted into what extent factors of neural cardiovascular control (for example, respiratory arrhythmia and blood pressure oscillations) are reflected in HRV. With help of cluster analysis methods applied to parameters extracted from the HRV power spectra, it was found that four different groups could be identified. The results indicated likely differences in neural cardiovascular control activity in psychiatric patients and normal subjects     

Linear and nonlinear analysis of normal and CAD-affected heart rate signals

Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.     

Kubios HRV – Heart rate variability analysis software

Kubios HRV is an advanced and easy to use software for heart rate variability (HRV) analysis. The software supports several input data formats for electrocardiogram (ECG) data and beat-to-beat RR interval data. It includes an adaptive QRS detection algorithm and tools for artifact correction, trend removal and analysis sample selection. The software computes all the commonly used time-domain and frequency-domain HRV parameters and several nonlinear parameters. There are several adjustable analysis settings through which the analysis methods can be optimized for different data. The ECG derived respiratory frequency is also computed, which is important for reliable interpretation of the analysis results. The analysis results can be saved as an ASCII text file (easy to import into MS Excel or SPSS), Matlab MAT-file, or as a PDF report. The software is easy to use through its compact graphical user interface. The software is available free of charge for Windows and Linux operating systems at http://kubios.uef.fi.     

Mining Physiological Conditions from Heart Rate Variability Analysis

This article presents a successfully developed methodology for mining physiological conditions from heart rate variability (HRV) analysis. The application of HRV analysis in both research and clinical settings has seen rapid development in the past decades. Unlike previous research, this study employed features derived from longterm monitoring of HRV indices, as these trends can best reflect the autonomic nervous system dynamics influenced by various physiological conditions. We proposed two methods for mining physiological conditions from HRV trends: a decision-tree learning method and a hybrid learning method that combines feature selection, feature extraction, and classifier construction processes. The proposed methods have been validated through a clinical case study: severity classification for Parkinson's disease. Our approach yielded classification accuracy greater than 90.0%, and high sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV).     

Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment

Mental stress is accompanied by dynamic changes in autonomic nervous system (ANS) activity. Heart rate variability (HRV) analysis is a popular tool for assessing the activities of autonomic nervous system. This paper presents a novel method of HRV analysis for mental stress assessment using fuzzy clustering and robust identification techniques. The approach consists of 1) online monitoring of heart rate signals, 2) signal processing (e.g., using the continuous wavelet transform to extract the local features of HRV in time-frequency domain), 3) exploiting fuzzy clustering and fuzzy identification techniques to render robustness in HRV analysis against uncertainties due to individual variations, and 4) monitoring the functioning of autonomic nervous system under different stress conditions. Our experiments involved 38 physically fit subjects (26 male, 12 female, aged 18-29 years) in air traffic control task simulations. The subjective rating scores of mental workload were assessed using NASA task load index. Fuzzy clustering methods have been used to model the experimental data. Further, a robust fuzzy identification technique has been used to handle the uncertainties due to individual variations for the assessment of mental stress. [ All rights reserved Elsevier].     

Autonomic function assessment using analysis of heart rate variability

Analysis of changes in heart rate can be useful in determining the state of various body systems. In particular the analysis of heart rate variability (HRV) is used in the assessment of autonomic function. This paper uses the discrete harmonic wavelet transform for a time-frequency analysis of HRV data to show changes in spectral power over time. Signals representing patient heart rate are presented, and methods for spectral and time-frequency analysis are described. Three sets of patient data are then analysed using these methods. The results show the potential of time-frequency analysis in the assessment of medical disorders, such as the sleep apnoea syndrome, where transient alterations in autonomic function occur.     

An absolute magnitude deviation of HRV for the prediction of prediabetes with combined artificial neural network and regression tree methods

Early diagnosis of prediabetes is an effective solution to the rising cases of diabetes around the world. The heterogeneous physiological characteristics of the ECG signal recorded from the heart make it challenging to implement an efficient diagnostic system. Therefore, this paper proposes a new approach to handling the heterogeneous characteristics of heart rate variability (HRV) with an absolute magnitude deviation analysis and an integrated machine learning technique for prediabetes prediction. We conducted an oral glucose tolerance test to acquire a resting-state ECG signal and the corresponding blood glucose value. We analyzed the HRV pattern from the ECG signal with a block-sliding window technique. We proposed a hybrid model to classify normal and prediabetes based on the extent of the absolute deviation of HRV values and avoiding a single point of failure. We adopted the model from the classification and regression tree (CART) and neural network (NN) algorithms. The experimental results reveal that when the blood glucose level increases, the maximum and range values of CARTHRV decreases while the minimum value increases. The proposed hybrid model had a better performance than the two methods with 100% sensitivity, specificity, and F1-score measures against CART and NN that recorded?<?100% for the same number of prediabetes in the training and test sets. The outcome from the analysis shows that the changes in blood glucose can be observed in ECG signals. The fast approximation of the proposed method to 100% accuracy suggests that it is possible to achieve the diagnosis of prediabetes and overcome the discrepancies in physiological signals among individuals.

     

基于时间序列分析的压气机喘振检测

喘振检测对于保障压气机安全运转具有重要意义。以压气机出口总压作为喘振检测特征信号,采用时间序列分析方法建立了AR模型。多步预测的误差和滞后现象显示AR模型难以预测突发性的喘振现象。在喘振发生时,残差方差急剧增大,模型参数变化显著,设定合适的门限可及时可靠地检测出喘振信号。     

Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal

In this paper, an effective paroxysmal atrial fibrillation (PAF) prediction algorithm is presented, which is based on analysis of the heart rate variability (HRV) signal. The proposed method consists of a preprocessing step for QRS detection and HRV signal extraction. In the next step, several features which can be used as markers for the prediction of PAF are extracted from the HRV signal. These features consist of spectrum features, bispectrum features, and non-linear features including sample entropy and Poincaré plot-extracted features. The spectrum features are able to discriminate the sympathetic and parasympathetic contents of the HRV signal, which are affected before PAF attacks. The bispectrum features are used in order to reveal information not presented on the spectral domain, and to detect quadratic phase coupled harmonics arising from non-linearities of the HRV signal. Moreover, the non-linear analysis can map the heart rate irregularities in the feature space and it leads to better understanding of the system dynamics before PAF attacks. In the final step, a support vector machine (SVM)-based classifier has been used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB). The obtained sensitivity, specificity, and positive predictivity were 96.30%, 93.10%, and 92.86%, respectively. The proposed methodology presents better results than the other existing approaches. The other important advantage of the proposed method when compared to the other approaches is that we do not need the both records of a subject to specify which episode preceding PAF events.     

Automatic ectopic beat elimination in short-term heart rate variability measurement

Our studies deal with fully automatic measurement of heart rate variability (HRV) in short term electrocardiograms. Presently, all existing HRV analysis programs require user intervention for ectopic beat identification, especially of supraventricular ectopic beats (SVE). This makes the HRV measurement in large, e.g. epidemiological studies problematic. In this paper, we present a fully automatic algorithm for the discrimination of the ventricular (VE) and SVE ectopic beats from the normal QRS complexes suited for a reliable HRV analysis. The QRS identification is based on the template matching method. The ectopic beats are identified based on several morphological and timing properties of the electrocardiogram (ECG) signal. The method incorporates several approaches and makes HRV analysis of large numbers of electrocardiograms feasible. It uses the template matching for the basic morphology check of the QRS complex and the P-wave, the timing information to avoid unnecessary ectopic beat checks and to adjust thresholds and it also looks for a special QRS morphology, which is common in VEs. We used a testing set of 69 electrocardiograms selected from a large number of recordings. The selected ECGs contained abnormalities including ectopic beats, right branch bundle block, respiratory arrhythmia, blocked atrial extrasystole, high amplitude and wide T-waves. The evaluation of our method showed a specificity of 0.99, supraventricular ectopic beat sensitivity of 0.99 and ventricular ectopic beat sensitivity of 0.98.     

单通道脑电信号眼电伪迹去除算法研究

由眨眼和眼动产生的眼电伪迹（Electrooculography,EOG）信号是脑电信号（Electroencephalography,EEG）中的主要噪声信号之一.目前,多通道脑电信号中眼电伪迹的去除算法已经较为成熟.而在单通道脑电信号的眼电伪迹去除中,由于采集通道数量较少且缺乏参考眼电信号,目前尚无十分有效的去除方法.本文提出一种基于小波变换（Wavelet transform,WT）、集合经验模态分解（Ensemble empirical mode decomposition,EEMD）和独立成分分析（Independent component analysis,ICA）的WT-EEMD-ICA单通道脑电信号眼电伪迹去除算法.实验表明：WT-EEMD-ICA算法有效地解决了单通道WT-ICA算法中的超完备问题,能够有效去除单通道脑电信号中的眼电伪迹,并且分离出的眼电伪迹成分与参考通道采集的眼电信号相关性较强.     

基于CEEMDAN-ICA的单通道脑电信号眼电伪迹滤除方法

传统盲源分离法不能解决欠定问题,且分离信号与源信号对应关系不确定.提出一种基于自适应噪声完备经验模态分解(CEEMDAN)和独立成分分析(ICA)相结合的脑电信号眼电伪迹自动去除方法.该方法首先将含伪迹脑电信号自适应分解成多维本征模态函数(IMF),以满足盲源分离方法对信号正定或超定要求,再对本征模态函数用ICA方法构建多维源信号,最后利用模糊熵阈值判据判别多维源信号中的伪迹信号,完成滤波并重构脑电信号.该方法相比于其他算法,能更好的去除眼电伪迹并保留原始信息,适合单通道脑电信号预处理.     

Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures

In present study, we proposed not only a novel methodology useful in developing the various features of heart rate variability (HRV), but also a suitable prediction model to enhance the reliability of medical examinations and treatments for coronary artery disease. In order to develop the various features of HRV, we analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on linear and nonlinear features of HRV. Forty-three control subjects and 64 patients with coronary artery disease participated in this study. In order to extract various features, we tested five classification methods and evaluated performance of classifiers. As a result, SVM and CMAR (gave about 72–88% goodness of accuracy) outperformed the other classifiers.     

Effect of missing RR-interval data on nonlinear heart rate variability analysis

The effects of missing RR-interval data on nonlinear heart rate variability (HRV) analysis were investigated using simulated missing data in actual RR-interval tachograms and actual missing RR-interval data. For the simulation study, randomly selected data (ranging from 0 to 100s) were removed from actual data in the MIT-BIH normal sinus rhythm RR-interval database. The selected data are considered as a simulated artefact section. In all, 7182 tachograms of 5-min duration were used for this analysis. For each missing interval, the analysis was performed by 100 Monte Carlo runs. Poincaré plot, detrended fluctuation, and entropy analysis were executed for the nonlinear HRV parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters, were calculated. In this process, the usefulness of reconstruction was considered, for which bootstrapping and several interpolation methods (nearest neighbour, linear, cubic spline, and piecewise cubic Hermite) were used. The rules for the reconstruction, derived from the results of these simulations, were evaluated with actual missing RR-interval data obtained from a capacitive-coupled ECG during sleep. In conclusion, nonlinear parameters, excepting Poincaré-plot-analysis parameters, may not be appropriate for the accurate HRV analysis with missing data, since these parameters have relatively larger error values than time- or frequency-domain HRV parameters. However, the analysis of the long-term variation for nonlinear HRV values can be available through applying the rules for the reconstruction obtained in this study.