新型三导联无线ECG设计与其处理算法研究

给出一种新型三导联无线ECG的设计方法。以便携式、小型化、低功耗为目标,分别以用于生物电势测量单通道24位模拟前端ADS1291、基于Cortex—M3微控制器STM32L162和2.4 GHz无线收发器nRF24L01作为心电检测的数据采集、数据采集控制与处理模块、无线通信模块。重点介绍了一种基于差分运算的QRS波检测方法,具有运算量低、正确率高等优点。制作了原理样机,并通过实验测试验证了该算法的有效性。     

基于DSP的EASI十二导联多功能Holter系统

一种以数字信号处理器(DSP)为核心,具有实时检测、无线传输和24小时心电数据连续记录功能的多功能Holter系统。在该系统中,通过EASI导联系统模块采集心电信号,然后采用小波变换算法对心电信号进行实时特征提取,并将心电数据存入MicroSD卡实现24小时心电数据记录;或者通过Zigbee无线传输模块将心电数据传输到电脑进行远程实时监测及十二导联心电数据的推导。     

Low-cost microcontroller driven ECG

This paper describes the design of a simple, low-cost device for surgeries, ambulances, etc. A 12-lead ECG is discussed. We shall describe the basic version and its hardware and software.     

Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram

Electrocardiogram (ECG) is characterized by a recurrent wave sequence of P, QRS and T-wave associated with each beat. The performance of the computer-aided ECG analysis systems depends heavily upon the accurate and reliable detection of these component waves. This paper presents an efficient method for the detection of P- and T-waves in 12-lead ECG using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and base line wander. SVM is used as a classifier for the detection of P- and T-waves. The algorithm is validated using original simultaneously recorded 12-lead ECG recordings from the standard CSE ECG database. Significant detection rate of 95.43% is achieved for P-wave detection and 96.89% for T-wave detection. The method successfully detects all kind of morphologies of P- and T-waves. The on-sets and off-sets of the detected P- and T-waves are found to be within the tolerance limits given in CSE library.     

Identification of QRS complexes in 12-lead electrocardiogram

A method for the detection of QRS complexes in 12-lead electrocardiogram (ECG) using support vector machine (SVM) is presented in this paper. Digital filtering techniques are used to remove base line wander and power line interference. SVM is used for the identification of QRS complexes in the processed signal. The performance of the algorithm is evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.75% of the identification rate. The percentage of false positive and false negative is 1.61% and 0.26%, respectively. The performance of the proposed algorithm is found to be better than published results of the other QRS detectors tested on the same database. The proposed method functions reliably even under the conditions of poor signal quality in the ECG data.     

可佩戴式远程心电采集终端的设计与实现

心电数据是诊断人体心脏状态的重要指标,在互联网大数据时代,远程医疗已成为一种趋势。为解决传统心电采集移动性和远程传输问题,而研制出一款可佩戴式远程心电采集终端。它由心电采集模块ADS1198、中央处理模块STM32、显示OLED液晶模块、USB模块、4G模块构成。能实时采集人体心电信号,并进行滤波处理,再通过OLED液晶模块大致显示心电波形。该设备具有通过USB模块传输数据至电脑实行近程心电数据管理或4G模块传输数据至医院监听端实现远程心电接收的功能。其体积为,重量仅为50g,工作电流仅为12.8mA。设备体积小巧,功耗低,便于佩戴,能采集医院分析病理的标准12导联心电数据,适用于在家庭中使用。     

Detection and delineation of P and T waves in 12-lead electrocardiograms

Abstract: This paper presents an efficient method for the detection and delineation of P and T waves in 12-lead electrocardiograms (ECGs) using a support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander. An SVM is used as a classifier for the detection and delineation of P and T waves. The performance of the algorithm is validated using original simultaneously recorded 12-lead ECG recordings from the standard CSE (Common Standards for Quantitative Electrocardiography) ECG multi-lead measurement library. A significant detection rate of 95.43% is achieved for P wave detection and 96.89% for T wave detection. Delineation performance of the algorithm is validated by calculating the mean and standard deviation of the differences between automatic and manual annotations by the referee cardiologists. The proposed method not only detects all kinds of morphologies of QRS complexes, P and T waves but also delineates them accurately. The onsets and offsets of the detected P and T waves are found to be within the tolerance limits given in the CSE library.     

Design of heart rate variability processor for portable 3-lead ECG monitoring system-on-chip

The worldwide population of people over the age of 65 has been predicted to more than double from 1990 to 2025. Therefore, ubiquitous health-care systems have become an important topic of research in recent years. In this paper, an integrated system for portable electrocardiography (ECG) monitoring, with an on-board processor for time–frequency analysis of heart rate variability (HRV), is presented. The main function of proposed system comprises three parts, namely, an analog-to-digital converter (ADC) controller, an HRV processor, and a lossless compression engine. At the beginning, ECG data acquired from front-end circuits through the ADC controller is passed through the HRV processor for analysis. Next, the HRV processor performs real-time analysis of time–frequency HRV using the Lomb periodogram and a sliding window configuration. The Lomb periodogram is suited for spectral analysis of unevenly sampled data and has been applied to time–frequency analysis of HRV in the proposed system. Finally, the ECG data are compressed by 2.5 times using the lossless compression engine before output using universal asynchronous receiver/transmitter (UART). Bluetooth is employed to transmit analyzed HRV data and raw ECG data to a remote station for display or further analysis. The integrated ECG health-care system design proposed has been implemented using UMC 90 nm CMOS technology.     

Development of a digital signal processor-based new 12-lead synchronization electrocardiogram automatic analysis system

This paper presents a digital signal processor (DSP)-based new multichannel electrocardiogram (ECG) system for 12-lead synchronization ECG automatic analysis in real-time with high sampling rate at 1000 Hz and 12-bits precision. Using the hardware structure of double-CPU based on Microprocessor (MPU) 89C55 and DSP TMS320F206 combines the powerful control ability of MPU with DSPs fast computation ability. Fully utilizing the double-CPUs resource, the system can distribute the reasonable CPU-time for the real-time tasks of multichannel synchronization ECG sampling, digital filter, data storing, waveform automatic analysis and print at high sampling rate. The digital ECG system has the advantages of simple structure, sampling with high speed and precision, powerful real-time processing ability and good quality. The paper discusses the system's principle and the skilful hardware design, also gives the ECG processing using the fast simple integer-coefficient filter method and the automatic calculation algorithms of the ECG parameters such as heart rate, P-R interval, Q-T interval and deflexion angle of ECG-axis etc. The system had been successfully tested and used in the ECG automatic analysis instrument.     

Discriminant analysis of multivariate time series: Application to diagnosis based on ECG signals

In analysing ECG data, the main aim is to differentiate between the signal patterns of healthy subjects and those of individuals with specific heart conditions. We propose an approach for classifying multivariate ECG signals based on discriminant and wavelet analyses. For this purpose we use multiple-scale wavelet variances and wavelet correlations to distinguish between the patterns of multivariate ECG signals based on the variability of the individual components of each ECG signal and on the relationships between every pair of these components. Using the results of other ECG classification studies in the literature as references, we demonstrate that our approach applied to 12-lead ECG signals from a particular database compares favourably. We also demonstrate with real and synthetic ECG data that our approach to classifying multivariate time series out-performs other well-known approaches for classifying multivariate time series.     

基于电容耦合的多导联心电监测系统研究

传统的非接触式心电监测系统在硬质印刷电路板构建的电容电极基础上,使用单导联方式进行心电监测,且仅根据心率变化进行心脏异常诊断,无法满足当前临床诊断标准.基于电容耦合原理,设计了一款多导联心电监测系统,将3个由导电织布构成的柔性电容电极集成于椅座背部,用于获取标准肢体导联Ⅰ、Ⅱ、Ⅲ和加压单极肢体导联 aVR、aVL、aVF等6导联心电信号.借由导电织布式柔性电容电极与人体表面较好的接触效应,可以有效减小运动伪影的产生.通过与硬质PCB电极输出的心电波形相比,使用本系统的患者在移动情况下输出的心电波形保持相对平稳状态,无明显运动伪影.系统实验证实了此新型多导联心电监测仪的有效性,不仅可以满足个人日常心电监测使用,而且适于长期动态心电监测.     

Automated ECG diagnostic P-wave analysis using wavelets

P-wave characteristics in the human ECG are an important source of information in the diagnosis of atrial conduction pathology. However, diagnosis by visual inspection is a difficult task since the P-wave is relatively small and noise masking is often present. This paper introduces novel wavelet characteristics derived from the continuous wavelet transform (CWT) which are shown to be potentially effective discriminators in an automated diagnostic process. Characteristics of the 12-lead ECG P-wave were derived using CWT and statistical methods. A normal control group and an abnormal (atrial conduction pathology) group were compared. The wavelet characteristics captured frequency, magnitude and variance components of the P-wave. The best individual characteristics (i.e. ones that significantly discriminated the groups) were entered into a linear discriminant analysis (LDA) for four different models: two-lead ECG, three-lead ECG, a derived three-lead ECG and a factor analysis solution consisting of wavelet characteristic loadings on the factors. A comparison was also made between wavelet characteristics derived form individual P-waves verses wavelet characteristics derived from a signal-averaged P-wave for each participant. These wavelet models were also compared to standard cardiological measures of duration, terminal force and duration divided by the PR segment. Results for the individual P-wave approach generally outperformed the standard cardiological measures and the signal-averaged P-wave approach. The best wavelet model on the basis of both classification performance and simplicity was the two-lead model that uses leads II and V1. It was concluded that the wavelet approach of automating classification is worth pursuing with larger samples to validate and extend the present study.     

远程无线心电监护仪的心电数据处理机制

鉴于远程无线心电实时监护的特点及嵌入式系统计算速度慢、内存空间小的限制,提出由心电数据采集、存储、发送、加密、压缩和分析组成的心电数据处理机制。该处理机制已应用于远程无线多生理参数实时监测与分析网络平台,能够通过GPRS/CDMA/3G无线网络将心电数据完整、准确、实时地发送到监护服务器并同时在监护仪上得到分析结果。     

基于统计特征和熵特征融合的心肌梗死辅助诊断方法

针对心肌梗死临床诊断过程中临床实用性和准确率不高的问题,提出一种基于12导联心电图（ECG）的心肌梗死的辅助诊断方法。首先,对12导联ECG信号进行去噪和数据增强处理;其次,分别对各导联ECG信号提取包含标准差、峰度系数、偏度系数的统计特征,以此反映信号的形态特征;同时,提取包含香农熵、样本熵、模糊熵、近似熵和排列熵的熵特征,以此表征ECG信号时间序列的时间与频谱复杂性、新模式产生的概率、规律性和不可预测性以及检测ECG信号的微小变化;然后,融合ECG信号的统计特征和熵特征;最后,基于随机森林算法在病人内和病人间两种模式下对算法进行分析和验证,并通过交叉验证防止过拟合。实验结果表明,病人内模式下算法准确率和F1值分别为99.98%和99.99%,病人间模式下算法准确率和F1值分别为94.56%和97.05%;与基于单导联ECG的诊断方法相比,采用12导联ECG诊断心肌梗死更符合医生临床诊断逻辑。     

心电模板构造方法及其在心电去噪中的应用

针对强噪声心电去噪,提出了基于心电模板的去噪方法。首先,将小波变换模极大值方法和信号相干平均技术相结合,构造出心电模板信号。然后给出了两种基于心电模板的强噪声心电去噪方法：直接重建法和间接相关法。最后将该方法与基于小波软、硬阈值去噪方法进行了对比,结果显示所得信号波形平滑度更好、信噪比更高。基于心电模板的去噪方法能够有效去除心电强噪声,为心电弱特征信息成分的准确提取奠定了基础。同时研究提供的心电模板构造方法也可用于其他准周期性生理信号,为强噪声生理信号去噪提供了一种有益思路。     

ECG variable cine: computer program for presentation of temporal changes in ECG variables over different number of ECG leads

The analysis of exercise electrocardiogram (ECG) is based on the alteration of the measured variables in the detection of coronary artery disease (CAD). In its existing form the analysis of the exercise ECG is laborious and requires much time. The temporal analysis of the ECG variable and the comparison between different phases of the exercise test is difficult and time consuming, especially the simultaneous examination of the variables over several leads. In this article we present a computer program, ECG Variable Cine, for the visualization of the temporal changes of values of exercise ECG variables over the selected ECG lead system. The program includes the stationary 3-D presentation for the variables' alteration simultaneously in all selected leads over the time of exercise test. In addition, the program determines two parameters; the average value of the variable over the selected leads at every sample moment, and the chronotropic index, a parameter that indicates heart rate response to exercise. According to the results the average value of ST-segment deviation at the end of the exercise over the leads and chronotropic index are clinically more competent than the maximum value of ST-segment depression in the detection of CAD.     

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.     

ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix

We propose a systematic ECG quality classification method based on a kernel support vector machine（KSVM） and genetic algorithm（GA） to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function（GRBF） is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function（MHWF）. GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search（GS） method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive（TP）, false positive（FP）, and classification accuracy were used as the assessment indices. For training database set A（1000 recordings）, the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were： TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B（500 recordings）, the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.     

基于卷积自编码神经网络的心电信号降噪

心电信号由于在采集过程中会受到外界环境的干扰导致其形态特征被严重淹没,从而对医生的诊断和远程智能分析造成干扰。基于此,提出了一种基于卷积自编码神经网络的心电信号降噪算法。该方法利用自编码器的编码、解码特性,通过卷积的方法构建深层神经网络来学习从含噪心电信号到干净心电信号的端对端映射。卷积层捕获心电信号的细节特征,同时消除噪声;解码部分能够对特征图进行上采样并恢复心电信号细节,从而得到干净的心电信号。实验中采用信噪比和均方根误差为指标,将该方法与小波阈值法、S变换法、BP神经网络法和指导滤波法进行比较。实验结果表明,该降噪方法整体降噪精度更优,同时信号的低频成分也得到了很好的保持。该方法可做到在消除心电信号中复杂噪声的同时完整保留心电信号的形态,为心血管疾病的智能诊断和心电图的特征检测奠定了基础。     

Design and Development of a Mobile Cardiac Rehabilitation System

In this article we present the design and implementation of a mobile cardiac monitoring system oriented to patients in Phase II and III of cardiac rehabilitation. The complete monitoring system involves both hardware and software design perspectives. At the hardware level, we present a T-shirt with a 12-lead ECG system and an embedded inertial sensor for the monitoring of activity and energy expenditure. At the software level, a modular cloud platform performs data processing to detect relevant cardiac events and to provide advanced visualization capabilities. As a case study, we have implemented our system at the Cardiac Rehabilitation program at Donostia University Hospital (Spain). Finally, the validation of the 12-lead ECG recording system is also presented and discussed.