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.
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.
Heart rate variability (HRV) analysis technology based on an autoregressive (AR) model is widely used in the assessment of autonomic nervous system function. The order of AR models has important influence on the accuracy of HRV analysis. This article presents a method to determine the optimum order of AR models. After acquiring the ECG signal of 46 healthy adults in their natural breathing state and extracting the beat-to-beat intervals (RRI) in the ECG, we used two criteria, i.e. final prediction error (FPE ) criterion to estimate the optimum model order for AR models, and prediction error whiteness test to decide the reliability of the model. We compared the frequency domain parameters including total power, power in high frequency (HF), power in low frequency (LF), LF power in normalized units and ratio of LF/HF of our HRV analysis to the results of Kubios-HRV. The results showed that the correlation coefficients of the five parameters between our methods and Kubios-HRV were greater than 0.95, and the Bland-Altman plot of the parameters was in the consistent band. The results indicate that the optimization algorithm of HRV analysis based on AR models proposed in this paper can obtain accurate results, and the results of this algorithm has good coherence with those of the Kubios-HRV software in HRV analysis.
Sleep status is an important indicator to evaluate the health status of human beings. In this paper, we proposed a novel type of unperturbed sleep monitoring system under pillow to identify the pattern change of heart rate variability (HRV) through obtained RR interval signal, and to calculate the corresponding sleep stages combined with hidden Markov model (HMM) under the no-perception condition. In order to solve the existing problems of sleep staging based on HMM, ensemble empirical mode decomposition (EEMD) was proposed to eliminate the error caused by the individual differences in HRV and then to calculate the corresponding sleep stages. Ten normal subjects of different age and gender without sleep disorders were selected from Guangzhou Institute of Respirator Diseases for heart rate monitoring. Comparing sleep stage results based on HMM to that of polysomnography (PSG), the experimental results validate that the proposed noninvasive monitoring system can capture the sleep stages S1–S4 with an accuracy more than 60%, and performs superior to that of the existing sleep staging scheme based on HMM.
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.
Objective Explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods From January 2022 to July 2022, screening was conducted among 118 patients based on inclusion/exclusion criteria. Fifty-eight patients were excluded, and 60 patients participated in this trial with informed consent and were randomly divided into a RIPC group (n=30) and a control group (n=30). Due to the cancellation of surgery, HRV data was missing. 7 patients in the control group were excluded, and 5 patients in the RIPC group were excluded, 23 patients in the final control group and 25 patients in the RIPC group were included in the analysis. Comparison of relevant indicators of heart rate variability (standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency component (LF), high frequency component (HF) and LF/HF) at 8 hours in the morning on the surgical day between two groups of patients. Results There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.
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.
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.
Lorenz plot (LP) method which gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze cardiac arrhythmias, and the morphologies and positions of the extracted attractors may reveal the underlying mechanisms of the onset and termination of arrhythmias. But automatic diagnosis is still impossible because it is lack of the method of extracting attractors by now. We presented here a methodology of attractor extraction and recognition based upon homogeneously statistical properties of the location parameters of scatter points in three dimensional LP (3DLP), which was constructed by three successive RR intervals as X, Y and Z axis in Cartesian coordinate system. Validation experiments were tested in a group of RR-interval time series and tags data with frequent unifocal premature complexes exported from a 24-hour Holter system. The results showed that this method had excellent effective not only on extraction of attractors, but also on automatic recognition of attractors by the location parameters such as the azimuth of the points peak frequency (APF) of eccentric attractors once stereographic projection of 3DLP along the space diagonal. Besides, APF was still a powerful index of differential diagnosis of atrial and ventricular extrasystole. Additional experiments proved that this method was also available on several other arrhythmias. Moreover, there were extremely relevant relationships between 3DLP and two dimensional LPs which indicate any conventional achievement of LPs could be implanted into 3DLP. It would have a broad application prospect to integrate this method into conventional long-time electrocardiogram monitoring and analysis system.
Heart rate is the most common index to directly monitor the level of physical stress by comparing the subject's heart rate with an appropriate "target heart rate" during exercise. However, heart rate only reveals the cardiac rhythm of the complex cardiovascular changes that take place during exercise. It is essential to get the dynamic response of the heart to exercise with various indices instead of only one single measurement. Based on the rest-workload alternating pattern, this paper screens the sensitive indices of exercise load from electrocardiogram (ECG) rhythm and waveform, including 4 time domain indices and 4 frequency domain indices of heart rate variability (HRV), 3 indices of waveform similarity and 2 indices of high frequency noise. In conclusion, RR interval (heart rate) is a reliable index for the realtime monitoring of exercise intensity, which has strong linear correlation with load intensity. The ECG waveform similarity and HRV indices are useful for the evaluation of exercise load.