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.
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.
The requirement for unconstrained monitoring of heartbeat during sleep is increasing, but the current detection devices can not meet the requirements of convenience and accuracy. This study designed an unconstrained ballistocardiogram (BCG) detection system using acceleration sensor and developed a heart rate extraction algorithm. BCG is a directional signal which is stronger and less affected by respiratory movements along spine direction than in other directions. In order to measure the BCG signal along spine direction during sleep, a 3-axis acceleration sensor was fixed on the bed to collect the vibration signals caused by heartbeat. An approximate frequency range was firstly assumed by frequency analysis to the BCG signals and segmental filtering was conducted to the original vibration signals within the frequency range. Secondly, to identify the true BCG waveform, the accurate frequency band was obtained by comparison with the theoretical waveform. The J waves were detected by BCG energy waveform and an adaptive threshold method was proposed to extract heart rates by using the information of both amplitude and period. The accuracy and robustness of the BCG detection system proposed and the algorithm developed in this study were confirmed by comparison with electrocardiogram (ECG). The test results of 30 subjects showed a high average accuracy of 99.21% to demonstrate the feasibility of the unconstrained BCG detection method based on vibration acceleration.
ObjectiveTo explore the predictive value of systolic pulmonary artery pressure (SPAP) on autonomic nerve excitation in patients with valvular disease, so as to provide reference for the formulation of clinical intervention plans. Methods The clinical data of patients with valvular disease who received surgical treatment in the General Hospital of Northern Theater Command from August 28, 2020 to February 3, 2021 were prospectively collected. According to the standard deviation of normal-to-normal R-R intervals (SDNN) of the heart rate variability (HRV) of the long-range dynamic electrocardiogram (ECG) 7 days before the operation, the patients were divided into three groups: a sympathetic dominant (SE) group (SDNN≤50 ms), a balance group (50 ms<SDNN<100 ms) and a parasympathetic dominant (PSE) group (SDNN≥100 ms). The correlation between the changes of echocardiographic indexes and autonomic nerve excitation among the groups and the predictive values were analyzed. Results A total of 186 patients were enrolled, including 108 males and 78 females aged 55.92±11.99 years. There were 26 patients in the SE group, 104 patients in the balance group, and 56 patients in the PSE group. The left anteroposterior diameter (LAD), left ventricular end diastolic inner diameter, ratio of peak E to peak A of mitral valve (Em/Am), left ventricular end diastolic volume, left ventricular end systolic volume and SPAP in the SE group were higher than those in the balance group (P<0.05), while peak A of tricuspid valve (At) and left ventricular ejection fraction (LVEF) were lower than those in the balance group (P<0.05). The LAD and Em/Am in the balance group were significantly higher than those in the PSE group (P<0.05). Multivariate analysis showed that patients in the SE group had lower At (right atrial systolic function declines), lower LVEF and higher SPAP than those in the balance group (P=0.04, 0.04 and 0.00). When HRV increased and parasympathetic nerve was excited in patients with valvular disease, Em/Am decreased (left atrial function and/or left ventricular diastolic function declined) with a normal LAD. Pearson analysis showed that there was a linear negative correlation between SPAP and SDNN, with a coefficient of −0.348, indicating that the higher SPAP, the lower HRV and the more excited sympathetic nerve. Receiver operating characteristic curve showed that when SPAP≥45.50 mm Hg (1 mm Hg=0.133 kPa), the sensitivity and specificity of sympathetic excitation in patients with valvular disease were 84.60% and 63.70%, respectively. ConclusionParasympathetic excitation is an early manifestation of the disease, often accompanied by decreased left atrial function and/or left ventricular diastolic function. Sympathetic nerve excitation can be accompanied by the increase of SPAP and the decrease of left ventricular and right atrial systolic function. SPAP has a unique predictive value for the prediction of autonomic nerve excitation in patients with valvular disease.
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.
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 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.
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.
Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.