基于AD8232的心电实时监测及分析系统设计

为了方便对患者心电信号进行实时监测,实现对心脏疾病的及时预防及诊断,利用一款基于ATmega328p微控制器的Arduino开发板、一块心电监测前端模块AD8232及上位机软件LabVIEW开发出一套心电实时监测系统,并利用LabVIEW设计出多种软件滤波方法来抑制心电信号中的噪声。由于心电信号的时频特性能提供反映患者心脏活动动态行为的信息,该系统还包括基于LabVIEW设计出的多种用于心电信号实时分析的程序,使被试心电信号所包含的生理特性能够及时地被分析出来。利用所开发的心电实时监测分析系统对被试的心电信号进行采集和分析,发现系统能够非常灵敏、准确地检测心电信号,并对信号噪声有着很好的抑制能力。此外系统能够对信号进行各式的实时分析,且分析结果可靠,能够运用于临床诊断。利用该系统对心电信号进行实时采集和分析,其测量结果准确、去噪效果良好、分析结果可靠,为今后心电实时监测分析系统的设计提供了借鉴。     

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time–frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time–frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.     

基于改进经验模态分解域内心动物理特征识别模式分量的心电信号重建

心电图(electrocardiogram,ECG)诊断心脏疾病的严格标准,要求有效地消除噪声并准确地重建ECG信号.经验模式分解(empirical mode decomposition,EMD)方法重建ECG信号中,模式混叠及重建采用模式分量的识别以经验为基础,导致重建ECG信号准确度降低,且方法不具有自适应和通用性.本文首先基于积分均值定理提出一种改进的EMD方法——积分均值模式分解(integral mean mode decomposition,IMMD)方法,经5000个高斯白噪声样本的蒙特卡罗法验证,IMMD方法比EMD具有更优多分辨率分析能力,能够有效地缓解模式混叠.其次,基于ECG信号内固有心动物理特征量识别重建ECG信号所采用的模式分量,具有现实物理意义,因此,方法具有自适应和通用性.经验证,提出方法重建47例ECG信号与原ECG信号的相关系数中:31例优于变分模式分解方法;33例优于Haar小波软阈值法;42例优于集总经验模式分解方法;45例优于EMD方法.相关系数均值为0.8904,方差为0.0071,表现稳定且最优.     

总体经验模态分解能量向量用于ECG能量分布的研究

总体经验模态分解(EEMD)改进了经验模态分解(EMD)存在的模态混叠问题, 依据信号自身的波动特点将信号分解, 特别适合非线性非平稳信号的分析处理. ECG信号能量分布有一定的规律, 疾病会引起能量分布的变化, 研究ECG能量分布的改变对心脏疾病的研究和临床诊断有重要意义. 本文将ECG信号通过EEMD方法分解为多个本征模态函数(IMF)分量, 观察IMF分量的波动规律, 指出了ECG信号在不同时间尺度上的波动特点和物理意义. 将IMF分量分别计算能量, 得到ECG的能量向量, 并对健康人和三种心脏疾病患者能量向量进行对比分析. 结果表明心脏疾病导致EEMD能量向量的高频分量显著降低, 尤其是p₁分量具有较好的区分度, 可以作为心脏疾病诊断的参考依据. 相比较传统的频域分析方法单纯关注频率而忽略信号自身特点和信号成分之间的相互作用, EEMD的分解结果依赖于ECG信号本身, 因此更能够反映ECG信号的真实情况, 揭示年龄和疾病对ECG能量分布的影响.     

Detection of determinism and randomness in time series: A method based on phase space overlap of attractors

The space overlap of an attractor reconstructed from a time series with a similarly reconstructed attractor from a random series is shown to be a sensitive measure of determinism. Results for the time series for Henon, Lorenz and Rössler systems as well as a linear stochastic signal and an experimental ECG signal are reported. The overlap increases with increasing levels of added noise, as shown in the case of Henon attractor. Further, the overlap is shown to decrease as noise is reduced in the case of the ECG signal when subjected to singular value decomposition. The scaling behaviour of the overlap with bin size affords a reliable estimate of the fractal dimension of the attractor even with limited data.     

基于形态滤波的心电信号去除基线漂移方法

去除基线漂移是心电信号预处理过程中的重要一步,常见方法具有计算量大、结果不佳等缺陷.本文运用形态学理论,结合心电信号特征,提出了基于形态学的心电信号基线漂移去除方法.该方法采用不同形状及尺寸的结构元素设计了两级形态学滤波器,分别对信号进行开闭、闭开级联组合运算.经验证,提出的方法能很好保持心电信号的特征形态,提高了信噪比,减小了均方差,有效去除了基线漂移噪声.     

Mass exponent spectrum analysis of human ECG signals and its application to complexity detection

The complexity of electrocardiogram (ECG) signal may reflect the physiological function and healthy status of the heart. In this paper, we introduced two novel intermediate parameters of multifractality, the mass exponent spectrum curvature and area, to characterize the nonlinear complexity of ECG signal. These indicators express the nonlinear superposition of the discrepancies of singularity strengths from all the adjacent points of the spectrum curve and thus overall subsets of original fractal structure. The evaluation of binomial multifractal sets validated these two variables were entirely effective in exploring the complexity of this time series. We then studied the ECG mass exponent spectra taken from the cohorts of healthy, ischemia and myocardial infarction (MI) sufferer based on a large sets of 12 leads’ recordings, and took the statistical averages among each crowd. Experimental results suggest the two values from healthy ECG are apparently larger than those from the heart diseased. While the values from ECG of MI sufferer are much smaller than those from the other two groups. As for the ischemia sufferer, they are almost of moderate magnitude. Afterward, we compared these new indicators with the nonlinear parameters of singularity spectrum. The classification indexes and results of total separating ratios (TSR, defined in the paper) both indicated that our method could achieve a better effect. These conclusions may be of some values in early diagnoses and clinical applications.     

Some regularity on how to locate electrodes for higher fECG SNRs

The electrocardiogram(ECG) recorded from the abdominal surface of a pregnant woman is a composite of maternal ECG, fetal ECG(f ECG) and other noises, while only the f ECG component is always needed by us. With different locations of electrode pairs on the maternal abdominal surface to measure f ECGs, the signal-to-noise ratios(SNRs) of the recorded abdominal ECGs are also correspondingly different. Some regularity on how to locate electrodes to obtain higher f ECG SNRs is needed practically. In this paper, 343 groups of abdominal ECG records were acquired from 78 pregnant women with different electrode pairs locating, and an appropriate extended research database is formed. Then the regularity on f ECG SNRs corresponding to different electrode pairs locating was studied. Based on statistical analysis, it is shown that the f ECG SNRs are significantly higher in certain locations than others. Reasonable explanation is also provided to the statistical result using the theories of the fetal cardiac electrical axis and the signal phase delay.     

Wavelet optimization for applying continuous wavelet transform to maternal electrocardiogram component enhancing

In the procedure of non-invasive fetal electrocardiogram(ECG) extraction, high-quality maternal R wave peak detection demands enhancing the maternal ECG component firstly. Among all the enhancing algorithms, the one based on the continuous wavelet transform(CWT) is very important and its effectiveness depends on the optimization of the used wavelet. However, up to now, there is still no clear conclusion on the optimal wavelet(including type and scale) for CWT to enhance the maternal ECG component of an abdominal ECG signal. To solve this problem, in this paper, we select several common used types of wavelets to carry out our research on what the optimal wavelets are. We first establish big-enough training datasets with different sampling rates and make a maternal QRS template for each signal in the training datasets.Second, for each type of selected wavelets, we find its optimal scale corresponding to each QRS template in a training dataset based on the principle of maximal correlation. Then calculating the average of all optimized wavelet scales results in the mean optimal wavelet of this type for the dataset. We use two original abdominal ECG databases to train and test the optimized mean optimal wavelets. The test results show that, as a whole, the mean optimal wavelets obtained are superior to the wavelets used in other publications for applying CWT to maternal ECG component enhancing.     

Clifford Wavelet Entropy for Fetal ECG Extraction

Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step, due to the wavelet/mutiwavelet processing, a denoising procedure is applied to separate the noised parts from the denoised ones. The denoised signal is assumed to be a mixture of both the MECG and the FECG. One of the well-known measures of accuracy in information processing is the concept of entropy. In the present work, a wavelet/multiwavelet Shannon-type entropy is constructed and applied to evaluate the order/disorder of the extracted FECG signal. The experimental results apply to a recent class of Clifford wavelets constructed in Arfaoui, et al. J. Math. Imaging Vis. 2020, 62, 73–97, and Arfaoui, et al. Acta Appl. Math. 2020, 170, 1–35. Additionally, classical Haar–Faber–Schauder wavelets are applied for the purpose of comparison. Two main well-known databases have been applied, the DAISY database and the CinC Challenge 2013 database. The achieved accuracy over the test databases resulted in Se = 100%, PPV = 100% for FECG extraction and peak detection.     

Intracavity power addition with a close-ended composite four-mirror cavity

We present a novel four-mirror cavity with two active gains to combine power intracavity and also give a detailed theoretical analysis of the combined gain. By using the effective field method, the four-mirror cavity with two gain media can be regarded as a linear resonator with one effective combined gain (ECG), and we procure a theoretical model of the ECG and deduce its exact analytical expression. When the two branch gains are close to each other, the combined gain can be reduced to their product, and the simplified presentation of ECG has been demonstrated. The combined output power which directly reflects the small signal ECG of the four-mirror cavity is studied experimentally, and the results are in good agreement with the theoretical ones.     

Electrocardiographic gating and monitoring in NMR imaging

ECG gating and monitoring during NMR imaging may be achieved reliably by applying the principles in this tutorial. In order to use the ECG signal both for triggering and for patient monitoring it must have a prominent R-wave, while at the same time must have little artifact from gradient switches or the Lorentz voltage across the aorta, and not be significantly distorted by gradient switching artifacts. The twin goals of no image artifacts and minimal ECG artifacts may be achieved by the following means: (1) using ECG electrodes with minimal metal, (2) selecting electrodes and cables with no ferrous metals, (3) placing the limb electrodes close together, (4) placing the line between the limb electrodes and the leg electrode parallel to the magnetic flux lines and, if possible, parallel to the transverse component of the gradient flux lines, (5) keeping the area between the limb electrodes and the leg electrode small, (6) placing that area in the center of the imager and (7) twisting or braiding the cables. Following these principles allows artifact-free images and reliable ECG monitoring during ECG-gated NMR imaging examinations.     

Accuracy and effectiveness of self-gating signals in free-breathing three-dimensional cardiac cine magnetic resonance imaging

Conventional multiple breath-hold two-dimensional(2D) balanced steady-state free precession(SSFP) presents many difficulties in cardiac cine magnetic resonance imaging(MRI). Recently, a self-gated free-breathing three-dimensional(3D) SSFP technique has been proposed as an alternative in many studies. However, the accuracy and effectiveness of selfgating signals have been barely studied before. Since self-gating signals are crucially important in image reconstruction, a systematic study of self-gating signals and comparison with external monitored signals are needed.Previously developed self-gated free-breathing 3D SSFP techniques are used on twenty-eight healthy volunteers. Both electrocardiographic(ECG) and respiratory bellow signals are also acquired during the scan as external signals. Self-gating signal and external signal are compared by trigger and gating window. Gating window is proposed to evaluate the accuracy and effectiveness of respiratory self-gating signal. Relative deviation of the trigger and root-mean-square-deviation of the cycle duration are calculated. A two-tailed paired t-test is used to identify the difference between self-gating and external signals. A Wilcoxon signed rank test is used to identify the difference between peak and valley self-gating triggers.The results demonstrate an excellent correlation(P = 0, R 0.99) between self-gating and external triggers. Wilcoxon signed rank test shows that there is no significant difference between peak and valley self-gating triggers for both cardiac(H = 0, P 0.10) and respiratory(H = 0, P 0.44) motions. The difference between self-gating and externally monitored signals is not significant(two-tailed paired-sample t-test: H = 0, P 0.90).The self-gating signals could demonstrate cardiac and respiratory motion accurately and effectively as ECG and respiratory bellow. The difference between the two methods is not significant and can be explained. Furthermore, few ECG trigger errors appear in some subjects while these errors are not found in self-gating signals.     

LOW-OSCILLATION COMPLEX WAVELETS

In this paper we explore the use of two low-oscillation complex wavelets—Mexican hat and Morlet—as powerful feature detection tools for data analysis. These wavelets, which have been largely ignored to date in the scientific literature, allow for a decomposition which is more “temporal than spectral” in wavelet space. This is shown to be useful for the detection of small amplitude, short duration signal features which are masked by much larger fluctuations. Wavelet transform-based methods employing these wavelets (based on both wavelet ridges and modulus maxima) are developed and applied to sonic echo NDT signals used for the analysis of structural elements. A new mobility scalogram and associated reflectogram is defined for analysis of impulse response characteristics of structural elements and a novel signal compression technique is described in which the pertinent signal information is contained within a few modulus maxima coefficients. As an example of its usefulness, the signal compression method is employed as a pre-processor for a neural network classifier. The authors believe that low oscillation complex wavelets have wide applicability to other practical signal analysis problems. Their possible application to two such problems is discussed briefly—the interrogation of arrhythmic ECG signals and the detection and characterization of coherent structures in turbulent flow fields.     

An adaptive correlation ratemeter: a new method for Doppler fetal heart rate measurements

For use in a cardiotocograph, ultrasound Doppler fetal signals have been thought to provide merely a limited reliability of the fetal heart rate record because of the difficulty of signal processing to obtain a consistent trigger, although the signal itself is easy to obtain with an excellent signal-to-noise ratio.However, the authors have developed a unique signal processing system using a correlation technique with an automatic adaptation algorithm, which solved almost all of the difficulties associated with the old Doppler cardiotocograph and showed that the ultrasound Doppler fetal signal can also provide heart rate records compatible to that obtained by a fetal ECG system.The authors are sure that their method is a most promising one in the coming age of microprocessor-oriented instrumentation.     

On the Variability of Heart Rate Variability—Evidence from Prospective Study of Healthy Young College Students

Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).     

The use of synthetic input sequences in time series modeling

In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure.     

基于符号相对熵的心电信号时间不可逆性分析

心电图(ECG)信号的时间不可逆性能够反映出心脏的生理功能和健康状态.从短时ECG信号中探测时间不可逆性特征具有重要的现实意义. 文章提出符号相对熵方法(先进行符号化处理,再分别计算它们的时间不可逆性),研究了从MIT-BIH标准数据库中提取的正常窦性心律(normal sinus rhythm,NSR)、心室纤颤(ventricular fibrillation,VF)、心脏猝死(sudden cardiac death,SCD)三种信号.结果表明,这三种信号的时间不可逆性有所不同:NSR信号的时间不可 关键词： 心电信号 相对熵 时间不可逆性     

Dynamic analysis of heartbeat rate signals of epileptics using multidimensional phase space reconstruction approach

The heartbeat rate signal provides an invaluable means of assessing the sympathetic-parasympathetic balance of the human autonomic nervous system and thus represents an ideal diagnostic mechanism for detecting a variety of disorders such as epilepsy, cardiac disease and so forth. The current study analyses the dynamics of the heartbeat rate signal of known epilepsy sufferers in order to obtain a detailed understanding of the heart rate pattern during a seizure event. In the proposed approach, the ECG signals are converted into heartbeat rate signals and the embedology theorem is then used to construct the corresponding multidimensional phase space. The dynamics of the heartbeat rate signal are then analyzed before, during and after an epileptic seizure by examining the maximum Lyapunov exponent and the correlation dimension of the attractors in the reconstructed phase space. In general, the results reveal that the heartbeat rate signal transits from an aperiodic, highly-complex behaviour before an epileptic seizure to a low dimensional chaotic motion during the seizure event. Following the seizure, the signal trajectories return to a highly-complex state, and the complex signal patterns associated with normal physiological conditions reappear.     

A Novel Filter Scheme of Data Processing for SQUID-Based Magnetocardiogram

We present a new filter scheme for magnetocardiogram （MCG） signal processing based on the quasi-periodic characteristic of the signals. The key points of this scheme are to determine the exact numbers of data points in each cardiac cycle by using electrocardiogram （ECG） data acquired simultaneously with the MCG signal and to normalize the MCG data sequence in each cycle into an identical length. Compared with conventional filters, the scheme has the advantage of more powerful noise suppression with less signal distortion. The desire for having high quality output signals from raw MCG data acquired in a simple shielded room or even in unshielded environment may be realized with the scheme.