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.
In order to solve imperfection of heart rate extraction by method of traditional ballistocardiogram (BCG), this paper proposes an improved method for detecting heart rate by BCG. First, weak cardiac activity signals are acquired in real time by embedded sensors. Local BCG beats are obtained by signal filtering and signal conversion. Second, the heart rate is estimated directly from the BCG beat without the use of a heartbeat template. Compared with other methods, the proposed method has strong advantages in heart rate data accuracy and anti-interference, and it also realizes non-contact online detection. Finally, by analyzing the data of more than 20,000 heart rates of 13 subjects, the average beat error was 0.86% and the coverage was 96.71%. It provides a new way to estimate heart rate for hospital clinical and home care.
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 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 effect of inhaled anticholinergics on heart rate recovery (HRR) in patients with stable chronic obstructive pulmonary disease (COPD). Methods Sixty clinically stable patients with stage Ⅱ-Ⅳ COPD according to the Global Initiative for Chronic Obstructive Lung Disease guidelines were recruited. HRR was analyzed in this study between 28 patients who had received tiotropium≥1 year and 32 patients who never used anticholinergics as control, so as to reflect parasympathetic reactivity of the heart. Results HRR was significantly lower in the subjects with tiotropium than that in the controls [(16±6) beats/min vs. (22±8) beats/min, P<0.05]. Multivariate regression analysis revealed that anticholinergics medication could be used as an independent predictor of HRR in the COPD patients. Conclusion Anticholinergics can affect cardiac autonomic function of stable COPD patients.
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.
目的 研究长期持续性心房颤动患者静息心率控制与脉搏波传导速度（PWV）的关系。 方法 序贯收集于2011年12月－2012年3月在四川大学华西医院心脏内科门诊就诊的长期持续性心房颤动患者84例，将人群按静息心率是否低于80次/min，分为标准组（心率＜80次/min）和对照组（心率≥80次/min），采用Pearson相关及多元线性回归分析方法，研究静息心率控制与PWV的关系。 结果 ①标准组人群的PWV显著低于对照组，而饮酒率显著高于对照组，差异皆有统计学意义（P＜0.05）。②Pearson相关分析显示静息心率与PWV存在线性相关关系（r=0.355，P=0.001）；多元线性回归分析显示，在调整了年龄、性别、BMI、收缩压、舒张压、吸烟、饮酒、空腹血糖、总胆固醇、甘油三酯、高密度脂蛋白、低密度脂蛋白等混杂因素影响后，心率与PWV仍独立相关。 结论 长期持续性心房颤动患者的静息心率控制不良与PWV升高关系密切。