高频谱心电信号的研究趋势

临床数据表明,与传统心电信号相比,高频心电信号在心肌梗塞、房室纤颤、心肌缺血、心肌炎、心室肥大、室性心动过速等患者中表现出高的敏感性,但不具有特异性。与传统心电信号研究相比,高频心电信号的研究报道较少。本文分析总结了高频心电信号的临床意义及研究中存在的问题,结合传统心电信号分析采用的方法,提出扩展高频心电信号研究的途径。     

基于小波变换的心电信号去噪综合算法

目的：研究去除心电信号中的基线漂移、工频干扰和肌电干扰等噪声,提高心电信号的自动识别和诊断精度。方法：利用Coif4小波对心电信号进行8尺度分解,采用小波分解重构法去除基线漂移,然后利用改进的小波闽值算法去除工频干扰和肌电干扰。结果：利用Matlab仿真工具,选择MIT-BIH心率失常数据库中信号进行验证,能有效去除这三种噪声,并且很好的保持R波的信息。结论：本算法在不丢失心电信号有用信息的前提下,可以较好的去除三种常见的噪声,可以用于心电信号自动分析之前的预处理。     

基于小波变换的心电信号去噪算法

目的:去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信号,避免噪声对心电信号特征点的识别和提取造成误判和漏判。方法:首先利用coif4小波对心电信号按Mallat算法进行分解,然后采用软、硬阈值折衷与小波重构的算法进行去噪。结果:采用MIT/BIH Arrhythmia Database中的心电信号进行仿真、验证,有效去除了三种常见的噪声信号。结论:本方法实时性好,为临床分析与诊断奠定了基础。     

早期心电异常检测系统的研制——心电积分图

心电积分图是对心电信号进行一系列积分转换，是分析心电信号早期变化的一种无创性方法，能把ＥＣＧ的相对面积和间变化放大，用于冠状动脉疾病引起的心电信号早期的检测。     

基于小波的心电信号有效性检测方法

目的：检测采集到的信号是否为有效心电信号,提高后续心电诊断和分析的准确率。方法：将采集到的信号进行预处理,即去噪处理,主要抑制基线漂移,50Hz工频及其谐波干扰和肌电干扰;取滑动窗长度为4s,检测该段内信号是否有效。为了验证算法的准确率及对不同心电波形是否具有普遍适用性,对MIT-BIH Arrhythmia Database中48个记录,CU及MIT-BIH Noise Stress Test Database中部分记录进行了仿真、验证。结果：仿真实验证明该方法能正确区分有效和无效信号,错检率较低,实现简单,适合实时处理。结论：本方法准确率高,能减少后续心电诊断和分析的计算量并提高准确率,特别是对室颤检测,符合心电分析的要求。     

心电信号的采集及R波同步检测

心电信号的R波识别在室速和房颤的同步除颤中具有十分重要的意义。传统的软件识别往往不能达到实时效果,而硬件识别则可以有效解决这一难题。本研究主要包括应用硬件电路构成的心电信号的采集系统和R波同步检测。硬件部分设计采用胸部双电极单导联输入心电信号,放大后再经微分电路和全波整流电路的处理,最后通过可选择的电压窗口比较器输出最终的R波信号检测结果。     

基于心电信号的飞行员应激评价模型的研究

本文基于教练机飞行员训练期间的心电信号,以人体瞬间应激水平为评价依据,通过采集特殊的飞行作业期间的飞行员的心电信号,计算心率以及心率变异性的相关指标,对个体产生瞬间应激的心电信号进行心率相关指标分析以及心率变异性的时域指标分析.同时对应激反应下心率曲线面积变化进行分析,采用多尺度算法,构建了应激强度随训练次数变化的函数关系,揭示了飞行训练应激强度变化的规律,提出了飞行训练及应激阶段的划分方法,建立了基于心电信号的飞行员应激评价的模型.     

一种基于小波变换的心电去噪算法

目的:去除心电信号采集过程中混入的工频干扰、肌电干扰和基线漂移等噪声信号,并能有效的保留心电特征信息.方法:通过小波变换将含噪的心电信号分解并重构得到不同尺度下的细节信号,在中小尺度上选取不同的门限值,并在QRS波群信息多的尺度上计算获得信息窗,对该尺度的信息窗内外采用不同的门限处理方式,在大尺度上直接重构出要去除的基线信息.结果:采用MIT/BIH Arrhythmia Database中的数据对算法进行了仿真验证,实现了三种主要干扰的去除,较好的保留了心电特征信息.结论:本方法效果较好,为后续的特征点识别奠定了基础.     

型人工心肺机反搏装置中心电QRS波检出系统的分析和设计

前言新型人工心肺机反搏装置是用微机进行智能控制的,而微机的智能控制是以心电QRS波的检出为前提的。由于人体体表的心电信号极其微弱,几乎被各种各样的干扰和伪迹所淹没,而且心电信号的大小和形状随个体、导联和病情的不同而不同。所以,要买现反搏的智能控制,必须设计一个QRS波检出系统,将QRS波放大整形至一定宽度的方波信号,然后送至微机的中断申请接口。就能为微机提供一个同步基准信号。长久以来,心电放大器和QRS波检出器的设计一直是医用电子学领域内的一个重要课题。国内外发表的许多文献介绍了多种多样的电路设计方案,并对几种典型电路进行     

基于SOPC技术的多路并行ECG实时检测分析系统的设计

针对目前心电监护设备微型化、实时性、高采样率、存储量大等实际需求,采用了一种基于最新的SOPC（System On a Programmable Chip）技术的心电检测系统的设计。将DSP和MCU的功能集成在一块FPGA上,在FPGA内部实现多路心电信号的并行处理,由SD卡记录较长时间的连续心电信号,并实现心电信号的实时分析和心律失常的预警等扩展功能。     

Analysis of ECG signal denoising method based on S-transform

Electrocardiogram (ECG), a noninvasive technique which is used generally as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. However in real situation, noise is often embedded with ECG signal during acquisition. In this paper, a novel ECG signal denoising technique is proposed using Stockwell transform (S-transform). This method is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises to visually inspected clean ECG recordings. The experimental results demonstrate that the proposed method shows the better signal to noise ratio (SNR), lower root mean square error (RMSE) and percent root mean square difference (PRD) compared to generally used ECG denoising method like wavelet transform.     

基于虚拟仪器技术的血管壁动态信息无创检测与辅助诊断系统

为了在体外无损地实现对血管壁动态信息、心电和心音信号等医学信息多参数的综合同步检测以及分析和处理,利用虚拟仪器技术设计了血管壁动态信息多参数的无创检测辅助诊断系统．该系统硬件平台由信号输入模块、信号调理模块以及采集卡等三大模块组成。软件系统由开发虚拟仪器的流行软件LabVIEW编写,实现了数据的采集、实时显示、分析处理和存储等。初步的临床检测结果证实了本无创检测系统的可行性和临床应用的前景,为功能性无创辅助诊断与血管壁弹性（硬化）程度有关的血管疾病提供了一种新的方法。     

Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization

Averaging signals in time domain is one of the main methods of noise attenuation in biomedical signal processing in case of systems producing repetitive patterns such as electrocardiographic (ECG) acquisition systems. This paper presents a comprehensive study of weighted averaging of ECG signal. Presented methods use criterion function minimization, partitioning of input set of data in the time domain as well as Bayesian and empirical Bayesian framework. The existing methods are described together with their extensions. Performance of all presented methods is experimentally evaluated and compared with the traditional averaging by using arithmetic mean and well-known weighted averaging methods based on criterion function minimization (WACFM).     

Recording of data and immediate interpretation of myoelectric activity of the stomach--electrogastrography]

Unlike other electrophysiological measurements such as electrocardiography (ECG) and electroencephalography (EEG), the cutaneous measurement of the electrical activity of the stomach (electrogastrography, EGG) is not an established clinical diagnostic procedure. To overcome common problems in acquiring very-low-frequency signals in the presence of large and unstable voltages we developed a real time data acquisition and analysis system based on linear-phase digital signal processing instead of analog filtering. FFT's computed on line from highly overlapping time sets are displayed as pseudo-3D or grey-scale graphic plots.     

A hybrid on-line ECG segmenting system for long-term monitoring.

This paper introduces a new hybrid ECG beat segmenting system, which can be applied in the processing unit of single-channel, long-term ECG monitors for the on-line segmentation of the ECG signal. Numerous ECG segmentation techniques are already existing and applied, however sufficiently robust and reliable methods currently require more than one ECG signal channel and quite complex computations, which are practically not feasible in stand-alone, low-cost monitors. Our new system approach presents a time domain segmentation technique based on a priori physiological and morphological information of the ECG beat. The segmentation is carried out after classifying the ECG beat, using the linear approximation of the filtered ECG signal and considering the pathophysiological properties as well. The proposed algorithms require moderate computational power, allowing the practical realization in battery powered stand-alone long-term cardiac monitors or small-sized cardiac defibrillators. The prototype version of the system was implemented in Matlab. The test and evaluation of the system was carried out with the help of reference signal databases.     

An Efficient Automated Algorithm for Distinguishing Normal and Abnormal ECG Signal

ObjectiveThe present study aims to simulate an alarm system for online detecting normal electrocardiogram (ECG) signals from abnormal ECG so that an individual's heart condition can be accurately and quickly monitored at any moment, and any possible serious dangers can be prevented.Materials and methodsFirst, the data from Physionet database were used to analyze the ECG signal. The data were collected equally from both males and females, and the data length varied between several seconds to several minutes. The heart rate variability (HRV) signal, which reflects heart fluctuations in different time intervals, was used due to the low spatial accuracy of ECG signal and its time constraint, as well as the similarity of this signal with the normal signal in some diseases. In this study, the proposed algorithm provided a return map as well as extracted nonlinear features of the HRV signal, in addition to the application of the statistical characteristics of the signal. Then, artificial neural networks were used in the field of ECG signal processing such as multilayer perceptron (MLP) and support vector machine (SVM), as well as optimal features, to categorize normal signals from abnormal ones.ResultsIn this paper, the area under the curve (AUC) of the ROC was used to determine the performance level of introduced classifiers. The results of simulation in MATLAB medium showed that AUC for MLP and SVM neural networks was 89.3% and 94.7%, respectively. Also, the results of the proposed method indicated that the more nonlinear features extracted from the ECG signal could classify normal signals from the patient.ConclusionThe ECG signal representing the electrical activity of the heart at different time intervals involves some important information. The signal is considered as one of the common tools used by physicians to diagnose various cardiovascular diseases, but unfortunately the proper diagnosis of disease in many cases is accompanied by an error due to limited time accuracy and hiding some important information related to this signal from the physicians' vision leading to the risks of irreparable harm for patients. Based on the results, designing the proposed alarm system can help physicians with higher speed and accuracy in the field of diagnosing normal people from patients and can be used as a complementary system in hospitals.     

Cardiovascular alterations in Macaca monkeys exposed to stationary magnetic fields: Experimental observations and theoretical analysis

Simultaneous measurements were made of the electrocardiogram (ECG) and the intraarterial blood pressure of adult male Macaca monkeys during acute exposures to homogeneous stationary magnetic fields ranging in strength up to 1.5 tesla. An instantaneous, field strength-dependent increase in the ECG signal amplitude at the locus of the T wave was observed in fields greater than 0.1 tesla. The temporal sequence of this signal in the ECG record and its reversibility following termination of the magnetic field exposure are consistent with an earlier suggestion that it arises from a magnetically induced aortic blood flow potential superimposed on the native T-wave signal. No measurable alterations in blood pressure resulted from exposure to fields up to 1.5 tesla. This experimental finding is in agreement with theoretical calculations of the magnetohydrodynamic effect on blood flow in the major arteries of the cardiovascular system.     

Dynalets: A new method for modelling and compressing biological signals. Applications to physiological and molecular signals

The biological information coming from electrophysiologic sensors like ECG, pulse sensor or from molecular signal devices like NMR spectrometry has to be visualized and manipulated in a compressed way for an efficient medical use by clinicians, if stored in scientific data bases or in personalized patient records repositories. Here, we define a new transform called Dynalet based on Liénard ordinary differential equations susceptible to model the mechanism at the source of the studied signal, and we propose to apply this new technique first to the modelling and compression of real biological periodic signals like ECG and pulse rhythm. We consider that the cardiovascular activity results from the summation of cellular oscillators located in the cardiac sinus node and we show that, as a result, the van der Pol oscillator (a particular Liénard system) fits well the ECG signal and the pulse signal. The reconstruction of the original signal (pulse or ECG) using Dynalet transform is then compared with that of Fourier, counting the number of parameters to be set for obtaining an expected signal-to-noise ratio. Then, we apply the Dynalet transform to the modelling and compression of molecular spectra obtained by protein NMR spectroscopy. The reconstruction of the original signal (peak) using Dynalet transform is again compared with that of Fourier. After reconstructing visually the peak, we propose to periodize the signal and give it to hear, the whole process being called the protein “stethoscope”.     

Hydrogen sulfide donor sodium hydrosulfide-improved heat tolerance in maize and involvement of proline

Hydrogen sulfide (H₂S) has long been considered as a phytotoxin, but nowadays as a cell signal molecule involved in growth, development, and the acquisition of stress tolerance in higher plants. In the present study, hydrogen sulfide donor, sodium hydrosulfide (NaHS), pretreatment markedly improved germination percentage of seeds and survival percentage of seedlings of maize under heat stress, and alleviated an increase in electrolyte leakage of roots, a decrease in tissue vitality and an accumulation of malondialdehyde (MDA) in coleoptiles of maize seedlings. In addition, pretreatment of NaHS could improve the activity of Δ¹-pyrroline-5-carboxylate synthetase (P5CS) and lower proline dehydrogenase (ProDH) activity, which in turn induced accumulation of endogenous proline in maize seedlings. Also, application of proline could enhance endogenous proline content, followed by mitigated accumulation of MDA and increased survival percentage of maize seedlings under heat stress. These results suggest that sodium hydrosulfide pretreatment could improve heat tolerance of maize and the acquisition of this heat tolerance may be involved in proline.