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.
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.
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.
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 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.
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.
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.