心电信号去噪的数学形态学滤波器

为探索验证一种基于数学形态滤波器的去除心电基线漂移和工频干扰的高性能滤波器设计方法,借鉴数学形态学一维信号滤波原理,提出自适应阈值ECG去噪算法的思路,讨论了3σ统计准则在ECG自适应阈值滤波中的作用,利用改进的算法对心电图中常见的工频干扰和基线漂移进行校正。通过对MIT-BIH心率变异数据库中多组数据的仿真验证研究,验证了该算法能有效实现心电信号的噪声预处理;数学形态学理论在心电信号处理中具有良好性能,是实时处理一维生物医学信号有潜力的工具。     

Characterization and Elimination of AC Noise in Electrocardiograms: A Comparison of Digital Filtering Methods

Diverse digital methods have been advanced previously to remove power line (AC) interference in the ECG. Representative notch filters, adaptive filters and a globally derived filter are surveyed in this study; their performances are compared on artificial signals as well as actual ECGs. The ECGs, recorded at four European medical centers, are from the Common Standards in Electrocardiography (CSE) ECG tape library. AC interference in these ECGs is shown to exhibit two qualities especially relevant to filter design: considerable deviations from a nominal 50 Hz frequency and substantial noise at higher harmonics. Some criteria and useful quantitative measures are suggested to evaluate AC digital filters.     

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。     

Kernel-based nonlinear dimensionality reduction for electrocardiogram recognition

The human heart is a complex system that reveals many clues about its condition in its electrocardiogram (ECG) signal, and ECG supervising is the most important and efficient way of preventing heart attacks. ECG analysis and recognition are both important and tempting topics in modern medical research. The purpose of this paper is to develop an algorithm which investigates kernel method, locally linear embedding (LLE), principal component analysis (PCA), and support vector machine(SVM) algorithms for dimensionality reduction, features extraction, and classification for recognizing and classifying the given ECG signals. In order to do so, a nonlinear dimensionality reduction kernel method based LLE is proposed to reduce the high dimensions of the variational ECG signals, and the principal characteristics of the signals are extracted from the original database by means of the PCA, each signal representing a single and complete heart beat. SVM method is applied to classify the ECG data into several categories of heart diseases. Experimental results obtained demonstrated that the performance of the proposed method was similar and sometimes better when compared to other ECG recognition techniques, thus indicating a viable and accurate technique.     

Estimation and removal of power line interference in the electrocardiogram: a comparison of digital approaches

Global filtering of AC interference in the digitized ECG is introduced as a new concept. Two different filters embodying a global approach are presented. One is based on a least-squares error fit, the other uses a special summation method. Both methods are compared with a local predictive filter by applying each filter to artificial signals and to real ECGs. A critical evaluation of the results is given.     

Examination of the ECG signal classification technique DEA-ELM using deep convolutional neural network features

Multimedia Tools and Applications - The accurate separation of ECG signals has become crucial to identify heart diseases. Machine learning methods are widely used to separate ECG signals. The aim...     

Application of independent component analysis in removing artefacts from the electrocardiogram

Routinely recorded electrocardiograms (ECGs) are often corrupted by different types of artefacts and many efforts have been made to enhance their quality by reducing the noise or artefacts. This paper addresses the problem of removing noise and artefacts from ECGs using independent component analysis (ICA). An ICA algorithm is tested on three-channel ECG recordings taken from human subjects, mostly in the coronary care unit. Results are presented that show that ICA can detect and remove a variety of noise and artefact sources in these ECGs. One difficulty with the application of ICA is the determination of the order of the independent components. A new technique based on simple statistical parameters is proposed to solve this problem in this application. The developed technique is successfully applied to the ECG data and offers potential for online processing of ECG using ICA.     

A switchable scheme for ECG beat classification based on independent component analysis

A switchable scheme is proposed to discriminate different types of electrocardiogram (ECG) beats based on independent component analysis (ICA). The RR-interval serves as an indicator for the scheme to select between the longer (1.0 s) and the shorter (0.556 s) data samples for the following processing. Six ECG beat types, including 13900 samples extracted from 25 records in the MIT-BIH database, are employed in this study. Three conventional statistical classifiers are employed to testify the discrimination power of this method. The result shows a promising accuracy of over 99%, with equally well recognition rates throughout all types of ECG beats. Only 27 ICA features are needed to attain this high accuracy, which is substantially smaller in quantity than that in the other methods. The results prove the capability of the proposed scheme in characterizing heart diseases based on ECG signals.     

A comparative analysis of principal component and independent component techniques for electrocardiograms

Principal component analysis (PCA) is used for ECG data compression, denoising and decorrelation of noisy and useful ECG components or signals. In this study, a comparative analysis of independent component analysis (ICA) and PCA for correction of ECG signals is carried out by removing noise and artifacts from various raw ECG data sets. PCA and ICA scatter plots of various chest and augmented ECG leads and their combinations are plotted to examine the varying orientations of the heart signal. In order to qualitatively illustrate the recovery of the shape of the ECG signals with high fidelity using ICA, corrected source signals and extracted independent components are plotted. In this analysis, it is also investigated if difference between the two kurtosis coefficients is positive than on each of the respective channels and if we get a super-Gaussian signal, or a sub-Gaussian signal. The efficacy of the combined PCA–ICA algorithm is verified on six channels V1, V3, V6, AF, AR and AL of 12-channel ECG data. ICA has been utilized for identifying and for removing noise and artifacts from the ECG signals. ECG signals are further corrected by using statistical measures after ICA processing. PCA scatter plots of various ECG leads give different orientations of the same heart information when considered for different combinations of leads by quadrant analysis. The PCA results have been also obtained for different combinations of ECG leads to find correlations between them and demonstrate that there is significant improvement in signal quality, i.e., signal-to-noise ratio is improved. In this paper, the noise sensitivity, specificity and accuracy of the PCA method is evaluated by examining the effect of noise, base-line wander and their combinations on the characteristics of ECG for classification of true and false peaks.     

Automatic diagnosis of cardiac arrhythmia in electrocardiograms via multigranulation computing

The automatic and accurate arrhythmia diagnosis in the electrocardiogram (ECG) signals is significant for cardiac health. Typically, the arrhythmia diagnosis is automatically detected depending on single-lead signals or a simple combination of multilead signals from the ECG. However, it ignores the inter-lead correlation and the significance of different leads for different heart beats detection, which decreases the performance of arrhythmia diagnosis. In this paper, arrhythmia diagnosis is converted to a problem of multigranulation computing in the view of granular computing, and thus different lead signals can be captured to improve the effectiveness of abnormal heart beats detection. To this end, multilead ECG signals are firstly granulated into different fuzzy information granules by the fuzzy equivalence relation. An objective decision-making model based on fuzzy set theory is then proposed for describing and analyzing these granulated multilead ECG signals, which brings a self-adaptive and unsupervised decision making. As a result, the significance and correlation of different leads are analyzed by granularity selection and granular structures to make a better decision for arrhythmia diagnosis. Extensive experimental results show that the proposed algorithm can significantly improve the performance of arrhythmia diagnosis, especially better robustness to several types of cardiac arrhythmia.     

Myocardial Infarction—Pinpointing the Key Indicators in the 12-Lead ECG Using Data Mining

In this paper we describe how data mining techniques were used in order to pinpoint the key indicators for myocardial infarction in the electrocardiogram (ECG) by determining existing trends in a large data set. In order to provide a test bed for the data mining techniques a data mining tool was developed so that the effectiveness of various data mining techniques could be determined. The material consisted of 2730 ECGs recorded at an emergency department. A total of 517 ECGs were recorded on patients suffering acute myocardial infarction. The remaining ECGs were defined as control ECGs. A subset of the material was used to train the data mining tool. After training, the data mining tool was able to pinpoint the key ECG indicators for myocardial infarction in the test set (duration and amplitude of theQwave andRduration in lead V2) and successfully determine which patients had suffered a heart attack.     

窦性心律失常心电序列的可视化研究

对心电信号序列与心血管疾病之间存在关系的探索是研究心脏病临床诊断的一类经典论题。心电图是检测心脏病的重要工具,目前已采集到长期的批量数据,对其进行处理和判别具有实际意义。利用变值心电测量系统,对窦性心律T波改变这一特殊心电数据和正常心电序列进行处理形成2D散点图谱,以可视化的形式展示这两类心电信号的分布特征和异同[1],与传统心电图相比,所提方法具有直观透明易懂的特点;同时也列举了不同测量变量值情况下的心电序列可视化结果。     

基于STM32便携式心电监护装置的设计

综合运用传感器技术、嵌入式技术和无线通信技术,设计了一种以STM32F103C8T6微处理器为核心的家用便携式心电监护装置.利用Pulse Sensor脉搏心率传感器采集心电信号,经STM32内置的模数转换器(ADC)处理得到数字信号.LCD屏用于实时显示心率值和心电图(ECG),心率数据通过蓝牙通信模块实时传输到手机...     

Integration of independent component analysis and neural networks for ECG beat classification

In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG.     

人体HRV信号的检测及其最大李雅普诺夫指数估算

介绍了一种便携式心电监测仪器,用于监测人心电(ECG)信号.根据获得的心电信号数据,采用小波变换技术进行心电R峰的准确定位,进而得到心率变异(Heart Rate Variability,HRV)信号序列.在对HRV信号进行相空间重构的基础上进行关联维、最大李雅普诺夫指数的估算.结果表明,健康者和心率不齐者的HRV信号的最大李雅普诺夫指数均为正值,但处于心率不齐状态HRV的最大李雅普诺夫指数低于健康状态的最大李雅普诺夫指数.     

基于ARM和WIFI技术的心电信号实时检测系统设计与研究

当前心脏疾病是引发我国人民死亡的头号杀手,而传统的心电信号采集系统及终端存在检测数据遗漏、成本高以及适用人群范围窄等问题;该文依据模块化、低能耗、高性能的原则,设计了一种基于ARM和WIFI技术的心电信号实时采集系统;给出了心电信号实时检测系统各功能模块的介绍以及功能实现流程,上位机使用TCP Client作为客户端,MCU作为下位机端,并通过WIFI技术作为通信媒介,设计了主控模块、心电心率采集模块、LCD显示模块以及通信模块,实现了对心电信号的实时采集与传输;系统测试结果验证了该心电信号实时检测系统的实用性、合理性及有效性,实验结果达到预期目标,能够有效提高检测结果准确性,为发现和防治心脏疾病提供一种实时、可靠的检测平台。     

基于体震信号的心率检测装置的设计与实现

设计并实现了一种基于体震信号的心率检测装置。心脏泵血所引起的人体和与其接触物体的震动,经过压力传感器转换为电信号,通过放大模块等信号处理电路,再由串行接口输出到PC机中进行预处理,最后,利用一种改进的心率检测算法得到心率。同步采集一路心电信号作为时间基准测量心率,并检验装置的准确性,实验结果表明:两者测得的心率基本相同。     

可穿戴心电信号采集与分析系统的设计与实现

针对传统心电采集设备的移动限制性以及佩戴的不舒适性,根据可穿戴计算特点,设计并实现了穿戴式心电采集与分析系统。系统采用自主研发的12/单导联心电采集模块进行心电信号采集,数据可存于采集设备或经3G网络传输到服务器端,同时所开发的软件可对心电图进行辅助病情分析,实现对佩戴人的心电监护。还研究并制作了插入式电极和织物电极,并通过二者的结合提高了采集心电信号的质量。实际佩戴和使用结果表明,使用插入式织物电极的可穿戴式心电采集设备具有良好的舒适性,心电信号波形的质量能够达到临床监控的要求。     

Detection of the P and T waves in an ECG

A method for the detection of the P and T waves, as well as the identification of their onset and offset boundaries in an ECG, is described in this paper. This method is based on a recently proposed "length" transformation, which exhibits some very interesting characteristics and can be utilized for one-channel or multichannel waveforms. The utilization of this transformation for the detection of the P and T waves in ECGs is exemplified in this paper. Experimental results are also given with real ECGs taken from the standard CSE ECG library.     

Multilead ECG analysis

This paper describes the results of our recent research in computer-assisted ECG/VCG interpretation. It comprises new developments which were initiated by the advent of relatively inexpensive microcomputers. Our previous systems performed an off-line analysis of ECGs. Currently, there is a trend to move computer power near to the patient and to provide on-line analysis of ECGs. Besides the advantage of the direct availability of the ECG interpretation, quality control will reduce the number of uninterpretable ECGs and hence the number of repeated recordings. This paper describes the requirements that were established for a system for on-line ECG analysis. The system is based on our modular approach, just like our off-line system, Modular ECG ANalysis System (MEANS). Changes in the methods and software had to be made mainly because of the simultaneity of all ECG leads and the concurrency of the processing tasks. Other modifications and extensions of the algorithms necessary to meet the requirements of on-line ECG interpretation especially those related to processing speed, are discussed, and evaluation results are presented.