Calculation of linear parameters, such as time-domain and frequency-domain analysis of heart rate variability (HRV), is a conventional method for assessment of autonomic nervous system activity. Nonlinear phenomena are certainly involved in the genesis of HRV. In a seemingly random signal the Poincaré plot can easily demonstrate whether there is an underlying determinism in the signal. Linear and nonlinear analysis methods were applied in the computer words inputting experiments in this study for physiological measurement. This study therefore demonstrated that Poincaré plot was a simple but powerful graphical tool to describe the dynamics of a system.
On the basis of Poincare scatter plot and first order difference scatter plot, a novel heart rate variability (HRV) analysis method based on scatter plots of RR intervals and first order difference of RR intervals (namely, RdR) was proposed. The abscissa of the RdR scatter plot, the x-axis, is RR intervals and the ordinate, y-axis, is the difference between successive RR intervals. The RdR scatter plot includes the information of RR intervals and the difference between successive RR intervals, which captures more HRV information. By RdR scatter plot analysis of some records of MIT-BIH arrhythmias database, we found that the scatter plot of uncoupled premature ventricular contraction (PVC), coupled ventricular bigeminy and ventricular trigeminy PVC had specific graphic characteristics. The RdR scatter plot method has higher detecting performance than the Poincare scatter plot method, and simpler and more intuitive than the first order difference method.
In this paper, a heart rate variability analysis system is presented for short-term (5 min) applications, which is composed of an electrocardiogram signal acquisition unit and a heart rate variability analysis unit. The electrocardiogram signal acquisition unit adopts various digital technologies, including the low-gain amplifier, the high-resolution analog-digital converter, the real-time digital filter and wireless transmission etc. Meanwhile, it has the advantages of strong anti-interference capacity, small size, light weight, and good portability. The heart rate variability analysis unit is used to complete the R-wave detection and the analyses of time domain, frequency domain and non-linear indexes, based on the Matlab Toolbox. The preliminary experiments demonstrated that the system was reliable, and could be applied to the heart rate variability analysis at resting, motion states. etc.
The present paper is to analyze the trend of sinus heart rate RR interphase sequence after a single ventricular premature beat and to compare it with the two parameters, turbulence onset (TO) and turbulence slope (TS). Based on the acquisition of sinus rhythm concussion sample, we in this paper use a piecewise linearization method to extract its linear characteristics, following which we describe shock form with natural language through cloud model. In the process of acquisition, we use the exponential smoothing method to forecast the position where QRS wave may appear to assist QRS wave detection, and use template to judge whether current cardiac is sinus rhythm. And we choose some signals from MIT-BIH Arrhythmia Database to detect whether the algorithm is effective in Matlab. The results show that our method can correctly detect the changing trend of sinus heart rate. The proposed method can achieve real-time detection of sinus rhythm shocks, which is simple and easily implemented, so that it is effective as a supplementary method.
Predicting the termination of paroxysmal atrial fibrillation (AF) may provide a signal to decide whether there is a need to intervene the AF timely. We proposed a novel RdR RR intervals scatter plot in our study. The abscissa of the RdR scatter plot was set to RR intervals and the ordinate was set as the difference between successive RR intervals. The RdR scatter plot includes information of RR intervals and difference between successive RR intervals, which captures more heart rate variability (HRV) information. By RdR scatter plot analysis of one minute RR intervals for 50 segments with non-terminating AF and immediately terminating AF, it was found that the points in RdR scatter plot of non-terminating AF were more decentralized than the ones of immediately terminating AF. By dividing the RdR scatter plot into uniform grids and counting the number of non-empty grids, non-terminating AF and immediately terminating AF segments were differentiated. By utilizing 49 RR intervals, for 20 segments of learning set, 17 segments were correctly detected, and for 30 segments of test set, 20 segments were detected. While utilizing 66 RR intervals, for 18 segments of learning set, 16 segments were correctly detected, and for 28 segments of test set, 20 segments were detected. The results demonstrated that during the last one minute before the termination of paroxysmal AF, the variance of the RR intervals and the difference of the neighboring two RR intervals became smaller. The termination of paroxysmal AF could be successfully predicted by utilizing the RdR scatter plot, while the predicting accuracy should be further improved.
Heart rate variability (HRV) is an important point to judge a person’s state in modern medicine. This paper is aimed to research a person’s fatigue level connected with vagal nerve based on the HRV using the improved Welch method. The process of this method is that it firstly uses a time window function on the signal to be processed, then sets the length of time according to the requirement, and finally makes frequency domain analysis. Compared with classical periodogram method, the variance and consistency of the present method have been improved. We can set time span freely using this method (at present, the time of international standard to measure HRV is 5 minutes). This paper analyses the HRV’s characteristics of fatigue crowd based on the database provided by PhysioNet. We therefore draw the conclusion that the accuracy of Welch analyzing HRV combining with appropriate window function has been improved enormously, and when the person changes to fatigue, the vagal activity is diminished and sympathetic activity is raised.
The ultrasound Doppler fetal heart rate measurement is the gold standard of fetal heart rate counting. However, the existing fetal heart rate extraction algorithms are not designed specifically to suppress the high maternal interference during the second stage of labor, and false detection occurrences are common during labor. With this background, a method combining time-frequency frame template library optimal selecting and non-linear template matching is proposed. The method contributes a template library, and the optimal template can be selected to match the signal frame. After the short-time Fourier transform of the signal, the difference between the signal and the template is optimized by leaky rectified linear unit (LReLU) function frame by frame. The heart rate was calculated from the peak of the matching curve and the heart rate was calculated. By comparing the proposed method with the autocorrelation method, the results show that the detection accuracy of the proposed method is improved by 20% on average, and the non-linear template matching of 23% samples is at least 50% higher than the autocorrelation method. This paper designs the algorithm by analyzing the characteristics of the interference and signal mixing. We hope that this paper will provide a new idea for fetal heart rate extraction which not only focuses on the original signal.
The linear analysis for heart rate variability (HRV), including time domain method, frequency domain method and timefrequency analysis, has reached a lot of consensus. The nonlinear analysis has also been widely applied in biomedical and clinical researches. However, for nonlinear HRV analysis, especially for shortterm nonlinear HRV analysis, controversy still exists, and a unified standard and conclusion has not been formed. This paper reviews and discusses three shortterm nonlinear HRV analysis methods (fractal dimension, entropy and complexity) and their principles, progresses and problems in clinical application in detail, in order to provide a reference for accurate application in clinical medicine.
The purpose of this study is to discuss the feasibility of establishing capsaicin pain model and the possibility to evaluate different degrees of pain by the heart rate variability (HRV). It also aims to investigate the changes of autonomic nervous activity of volunteers during the process of pain caused by capsaicin. A total of 30 volunteers were selected, who were physically and mentally healthy, into the study. To assess the effects of capsaicin on the healthy volunteers, we recorded the Visual Analogue Scale (VAS) scores after the capsaicin stimulus. Additionally, the electrocardiogram signals and HRV analysis index before and after stimulating were also recorded, respectively. More specifically, the HRV analysis indexes included the time domain index, the frequency domain index, and the nonlinear analysis index. The results demonstrated that the activity of the autonomic nerves was enhanced in the process of capsaicin stimulus, especially for the sympathetic nerve, which exhibited a significantly differences in HRV. In conclusion, the degree of pain can be reflected by the HRV. It is feasible to establish a capsaicin pain model. And in further experiments, HRV analysis could be used as a reference index for quantitative evaluation of pain.
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.