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.
Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: −0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
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.
Based on the imaging photoplethysmography (iPPG) and blind source separation (BSS) theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i.e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis (ICA) theory and joint approximate diagonalization of eigenmatrices (JADE) algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of Bland-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2.06 beat/min. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the remote and non-contact measurement of multi-parameter physiological measurements.
Objective To systematically review the influence of tight heart rate (HR) control on the efficacy of perioperative β-blockade, and discuss the effective measures of perioperative myocardial protection. Methods We searched the PubMed, OVID, EMbase, the Cochrane Library and Chinese Biomedical Database (CBM) for randomized controlled trials on evaluating perioperative β-blockers after noncardiac surgery. The quality of the included studies was evaluated by the method recommended by the Cochrane Collaboration. Meta-analyses was conducted by using the Cochrane Collaboration’s RevMan software. Results Thirteen RCTs including 11 590 patients were included. The combined results of all studies showed cardioprotective effect of β-blockers (OR=0.64, 95%CI 0.50 to 0.80, P=0.000 1), with considerable heterogeneity among the studies (I2=57%). However, grouping the trials on the basis of maximal HR showed that trials where the estimated maximal HR was 100 bpm were associated with cardioprotection (OR=0.37, 95%CI 0.26 to 0.52, Plt;0.000 01) whereas trials where the estimated maximal HR was 100 bpm did not demonstrate cardioprotection (OR=1.13, 95%CI 0.81 to 1.59, P=0.48) with no heterogeneity (I2=0%). Conclusion The evidence suggests that effective control of HR is important for achieving cardioprotection and that administration of β-blockers does not reliably decrease HRs in all patients. Judicious use of combination therapy with other drugs may be necessary to achieve effective postoperative control of HR.
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.
【摘要】 目的 探讨急性脑梗死对心脏自主神经活性的影响。 方法 Wistar大鼠32只随机分为正常组、假手术组和脑梗死组，脑梗死组用线栓法行右侧大脑中动脉阻塞。脑梗死组和假手术组于术前及术后24 h作心率变异性（HRV）检测，同时检测正常组HRV，将3组的HRV指标进行比较。实验终点取各组心肌组织检测儿茶酚胺和神经肽Y（NPY），进行组间比较。 结果 术后24 h脑梗死组和正常组、假手术组相比，窦性心搏间期标准差、均方根，总功率谱、高频功率谱（HF）、低频功率谱（LF）降低，差异有统计学意义。3组比较LF/HF和分数维无明显差异。脑梗死组心肌组织去甲肾上腺素（NA）和NPY高于正常组和假手术组。 结论 脑梗死引起心脏自主神经总活性降低、自主神经功能受损，自主神经末梢去甲肾上腺素和NPY的异常分泌可能是重要的原因。【Abstract】 Objective To investigate the effect of acute cerebral infarction on cardiac autonomic nervous activity. Methods A total of 32 Wistar rats were divided into normal group, sham operation group and infarction group by random. Experimental cerebral infarction in Wistar rats was induced by intraluminal occlusion of middle cerebral artery. About 24 hours after the occlusion or 24 hours after sham operation, the heart rate variability (HRV) sequences were measured, and the HRV values in the three groups were compared. The levels of catecholamine and neuropeptide (NPY) in myocardium were measured. Results At the 24th hour after the occlusion, the standard deviation and root mean square standard deviation of R-R interval, the total power, high frequency (HF) and low frequency (LF) in infarction group were lower than those in normal and sham operation group. LF/HF and fractal dimension did not differ much among the three groups. The levels of noradrenaline and NPY in myocardium in infarction group were higher than those in the other groups. Conclusion It is suggested that acute cerebral infarction may cause the decrease of autonomic nervous activity and damage of the autonomic nervous function; the abnormal secretion of noradrenalin in autonomic nerve ending and NPY may be the important reasons.
Objective To systematically review the effect of percutaneous acupoint electrical stimulation (TEAS) on heart rate variability (HRV). Methods The PubMed, Embase, Ovid MEDLINE, Cochrane Library, CNKI, WanFang Data, VIP, and CBM databases were electronically searched to collect randomized controlled trials (RCTs) on the effects of percutaneous acupoint electrical stimulation on heart rate variability from inception to February 28, 2023. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using RevMan 5.4 software. Results A total of 14 RCTs involving 719 patients were included. The results of meta-analysis showed that SDNN (MD=12.95, 95%CI 9.18 to 16.72, P<0.01), RMSSD (MD=1.81, 95%CI 0.10 to 3.53, P=0.04), pNN50 (MD=1.75, 95%CI 1.02 to 2.48, P<0.01), HF (SMD=0.27, 95%CI 0.01 to 0.52, P=0.04), LF/HF (MD=−0.07, 95%CI −0.12 to −0.03, P<0.01), ln-LF (MD=0.63, 95%CI 0.25 to 1.01, P<0.01), ln-HF (MD=1.05, 95%CI 0.60 to 1.49, P<0.01), mean RR (MD=−11.86, 95%CI −21.77 to −1.96, P=0.02), and HR (SMD=−0.43, 95%CI −0.66 to −0.20, P<0.01) all showed improvement compared with the control group. However, there were no significant differences between the two groups in LF (SMD=0.15, 95%CI −0.10 to 0.40, P=0.23), LF norm (SMD=0.24, 95%CI −0.10 to 0.58, P=0.16) or HF norm (SMD=0.25, 95%CI −0.47 to 0.97, P=0.5). TEAS on PC6: SDNN, pNN50, HF, LF/HF, LF norm, HF norm, ln-LF, ln-HF, and HR all showed improvement compared with the control group. However, there were no significant differences between the two groups in RMSSD, LF, or RR interval. Conclusion This study supports the improvement of heart rate variability by transcutaneous acupoint electrical stimulation and PC6 acupoint selection. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [–4.78, 4.78] beats per minute, and a consistency error of –0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.