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.
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.
Heart rate variability (HRV) is the difference between the successive changes in the heartbeat cycle, and it is produced in the autonomic nervous system modulation of the sinus node of the heart. The HRV is a valuable indicator in predicting the sudden cardiac death and arrhythmic events. Traditional analysis of HRV is based on a multi-electrocardiogram (ECG), but the ECG signal acquisition is complex, so we have designed an HRV analysis system based on photoplethysmography (PPG). PPG signal is collected by a microcontroller from human’s finger, and it is sent to the terminal via USB-Serial module. The terminal software not only collects the data and plot waveforms, but also stores the data for future HRV analysis. The system is small in size, low in power consumption, and easy for operation. It is suitable for daily care no matter whether it is used at home or in a hospital.
Objective To investigate the changes and clinical relationship of plasma adrenomedullin( ADM) , atrial natriuretic polypeptide( ANP) , and heart rate variability( HRV) in patients with obstructive sleep apnea-hypopnea syndrome ( OSAHS) . Methods Seventy-five inpatients with OSAHS were enrolled in this study. According to the apnea hypopnea index ( AHI) by polysomnography, the subjects were divided into a mild group, a moderate group, and a severe group. Meanwhile, HRV was screened bydynamic electrocardiogram in sleep laboratory. HRV parameters were obtained including LF ( low frequency power) , HF( high frequency power) , pNN50( percentage of NN50 in the total number of N-N intervals) ,SDNN( standard deviation of the N-N intervals) , rMSSD( square root of the mean squared differences of successive N-N intervals ) . Plasma levels of ADM/ANP were measured by radioimmunoassay. Results The levels of SDNN ( P lt;0. 05) , rMSSD, pNN50, LF ( P lt; 0. 05) and HF were gradually reduced, and the levels of ADM ( P lt;0. 05) and ANP ( P lt; 0. 05) were increased with increasing severity of OSAHS. Linear correlation analysis demonstrated that SDNN was negatively correlated with ADM( r = - 0. 423, P lt;0. 05)and ANP( r = - 0. 452, P lt; 0. 05) , and LF was also negatively correlated with ADM( r = - 0. 348, P lt;0. 05) . Conclusion Lower HRV is associated with more sever OSAHS, and it may be modulated neurohumorally by ADM and ANP.
ObjectiveTo study the relationship between preoperative heart rate variability (HRV) and postoperative atrial fibrillation (POAF) after off-pump coronary artery bypass grafting (OPCAB). MethodsA retrospective analysis was performed on the clinical data of 290 patients who were admitted to the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command from May to September 2020 and received OPCAB. There were 217 males and 73 females aged 36-80 years. According to the incidence of POAF, the patients were divided into two groups: a non-atrial fibrillation group (208 patients) and an atrial fibrillation group (82 patients). The time domain and frequency domain factors of mean HRV 7 days before operation were calculated: standard deviation of all normal-to-normal intervals (SDNN), root mean square of successive differences, percentage difference between adjacent normal-to-normal intervals that were greater than 50 ms, low frequency power (LF), high frequency power (HF), LF/HF. ResultsThe HRV value of patients without POAF was significantly lower than that of patients with POAF (P<0.05). The median SDNN of the two groups were 78.90 ms and 91.55 ms, respectively. Age (OR=3.630, 95%CI 2.015-6.542, P<0.001), left atrial diameter (OR=1.074, 95%CI 1.000-1.155, P=0.046), and SDNN (OR=1.017, 95%CI 1.002-1.032, P=0.024) were independently associated with the risk of POPAF after OPCAB. Conclusion SDNN may be an independent predictor of POAF after OPCAB.
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.
目的:研究老年患者依托咪酯靶控输注时不同BIS值(脑电双频指数)的HRV(心率变异性)的变化情况,探讨不同镇静深度与HRV之间的关系。方法:选择65岁以上行门诊胃镜检查患者30例,随机分为3组,A组BIS45~55,B组55~65,C组65~75,各组均在麻醉前、麻醉诱导后,术中、术毕监测BIS、HRV及血液动力学指标。结果:A组各监测HRV明显降低(Plt;0.05),B组仅有轻度下降(Pgt;0.05),C组明显升高(Plt;0.05)。结论:患者镇静深度BIS55~65时,即可明显抑制内镜操作刺激所致的HRV变化,是临床较为合适的镇静深度,可显著降低老年患者交感神经活性、交感/迷走神经均衡性和自主神经总张力,利于机体血液动力学稳定。
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.