XML-BSPM: an XML format for storing Body Surface Potential Map recordings

Background  

The Body Surface Potential Map (BSPM) is an electrocardiographic method, for recording and displaying the electrical activity of the heart, from a spatial perspective. The BSPM has been deemed more accurate for assessing certain cardiac pathologies when compared to the 12-lead ECG. Nevertheless, the 12-lead ECG remains the most popular ECG acquisition method for non-invasively assessing the electrical activity of the heart. Although data from the 12-lead ECG can be stored and shared using open formats such as SCP-ECG, no open formats currently exist for storing and sharing the BSPM. As a result, an innovative format for storing BSPM datasets has been developed within this study.     

融合单导联心电图传统与深度特征的常见心律失常识别方法研究

心律失常是心血管疾病中最为常见的疾病类型之一.基于便携式设备,对少数导联的心电图进行长期智能监护,有利于提高心律失常的检出率,但产生的海量长程ECG数据会给临床医生带来极大的工作负担,也容易导致漏检和误判.为此,提出一种融合单导联心电图传统与深度特征的常见心律失常自动识别方法.首先针对常见心律失常,提取频域、时域和形态...     

Deriving the Standard 12-lead ECGbased on Weighed Epicardial Potential

INTRODUCTION　　 Many methods are routinely used in diagnosing and locating myocardial ischemiaand myocardial infarction,such as the standard 1 2 -lead ECG and body surfaceisopotential maps.All of them are to measure multi-pointbody surface potentialsnoni…     

Screening for left ventricular systolic dysfunction using GP-reported ECGs

BACKGROUND: Diagnostic echocardiography has poor access for patients with suspected heart failure. Pre-echocardiography screening with electrocardiograms (ECGs) is recommended as a means of targeting this scarce resource. There are data to support this policy when ECGs are interpreted by cardiologists but not by GPs. AIM: To assess the value of GP-reported ECGs as a pre-echocardiography screening test for left ventricular systolic dysfunction (LVSD). DESIGN OF STUDY: Cross-sectional study of GPs' ECG reporting skills. SETTING: General practice, NHS in Scotland. METHOD: A randomly selected, stratified sample of 123 Scottish GPs reviewed 180 ECGs (100 abnormal, 50 normal and 30 duplicate) from 150 patients with suspected heart failure. Forty-one patients had LVSD on echocardiography. GPs were required to categorise ECGs as normal or abnormal. RESULTS: Mean sensitivity was 0.94 (95% CI = 0.92 to 0.95). Mean specificity 0.58 (95% CI = 0.56 to 0.60). Mean positive predictive value (PPV) was 0.47 (95% CI = 0.46 to 0.48). Mean negative predictive value (NPV) was 0.96 (95% CI = 0.95 to 0.97). Mean likelihood ratio was 2.39 (95% CI = 2.28 to 2.50). Seventy of 123 (57%) GPs achieved sensitivity of 0.9 and specificity of 0.5 for the detection of LVSD. CONCLUSION: Most Scottish GPs have the skills to perform pre-echocardiography screening ECGs in patients with suspected LVSD. However, differences in ECG reporting performance between individual GPs will result in widely varying referral rates for echocardiography and differences in the detection rate of LVSD. The implications of these findings need to be considered when heart failure diagnostic services are being developed.     

Mathematical Modeling of Electrocardiograms: A Numerical Study

This paper deals with the numerical simulation of electrocardiograms (ECG). Our aim is to devise a mathematical model, based on partial differential equations, which is able to provide realistic 12-lead ECGs. The main ingredients of this model are classical: the bidomain equations coupled to a phenomenological ionic model in the heart, and a generalized Laplace equation in the torso. The obtention of realistic ECGs relies on other important features—including heart–torso transmission conditions, anisotropy, cell heterogeneity and His bundle modeling—that are discussed in detail. The numerical implementation is based on state-of-the-art numerical methods: domain decomposition techniques and second order semi-implicit time marching schemes, offering a good compromise between accuracy, stability and efficiency. The numerical ECGs obtained with this approach show correct amplitudes, shapes and polarities, in all the 12 standard leads. The relevance of every modeling choice is carefully discussed and the numerical ECG sensitivity to the model parameters investigated.     

Application of computer modelling and lead field theory in developing multiple aimed impedance cardiography measurements

Conventional impedance cardiography (ICG) methods estimate parameters related to the function of the heart from a single waveform that reflects an integrated combination of complex sources. We have previously developed methods and tools for calculating measurement sensitivity distributions of ICG electrode configurations. In this study, the methods were applied to investigate the prospects of recording multiple aimed ICG waveforms utilizing the 12-lead electrocardiography (ECG) electrode locations. Three anatomically realistic volume conductor models were used: one based on Visible Human Man cryosection data and two on magnetic resonance (MR) images representing end diastolic and end systolic phases of the cardiac cycle. Based on the sensitivity distributions obtained, 236 electrode configurations were selected for preliminary clinical examination on 12 healthy volunteers and 9 valvular patients. The model study suggested that a variety of configurations had clearly enhanced sensitivity to the cardiovascular structures as compared to conventional ICGs. Simulation data and clinical experiments showed logical correspondence supporting the theoretically predicted differences between the configurations. Recorded 12-lead ICG signals had characteristic waveforms and landmarks not coinciding with those of conventional ICG. Furthermore, configurations showing resemblance to invasive data and morphological variations in disease are of interest. The results indicate the applicability of the modelling approach in developing ICG measurement configurations. However, the level of clinical relevance and potential of the 12-lead method remains to be explored in studies employing dynamic modelling and acquisition of invasive reference data.     

Application of computer modelling and lead field theory in developing multiple aimed impedance cardiography measurements

Conventional impedance cardiography (ICG) methods estimate parameters related to the function of the heart from a single waveform that reflects an integrated combination of complex sources. We have previously developed methods and tools for calculating measurement sensitivity distributions of ICG electrode configurations. In this study, the methods were applied to investigate the prospects of recording multiple aimed ICG waveforms utilizing the 12-lead electrocardiography (ECG) electrode locations. Three anatomically realistic volume conductor models were used: one based on Visible Human Man cryosection data and two on magnetic resonance (MR) images representing end diastolic and end systolic phases of the cardiac cycle. Based on the sensitivity distributions obtained, 236 electrode configurations were selected for preliminary clinical examination on 12 healthy volunteers and 9 valvular patients. The model study suggested that a variety of configurations had clearly enhanced sensitivity to the cardiovascular structures as compared to conventional ICGs. Simulation data and clinical experiments showed logical correspondence supporting the theoretically predicted differences between the configurations. Recorded 12-lead ICG signals had characteristic waveforms and landmarks not coinciding with those of conventional ICG. Furthermore, configurations showing resemblance to invasive data and morphological variations in disease are of interest. The results indicate the applicability of the modelling approach in developing ICG measurement configurations. However, the level of clinical relevance and potential of the 12-lead method remains to be explored in studies employing dynamic modelling and acquisition of invasive reference data.     

ECG signal denoising and baseline wander correction based on the empirical mode decomposition

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Two dominant artifacts present in ECG recordings are: (1) high-frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes; (2) baseline wander (BW) that may be due to respiration or the motion of the patients or the instruments. These artifacts severely limit the utility of recorded ECGs and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG enhancement. In this paper, we propose a new ECG enhancement method based on the recently developed empirical mode decomposition (EMD). The proposed EMD-based method is able to remove both high-frequency noise and BW with minimum signal distortion. The method is validated through experiments on the MIT-BIH databases. Both quantitative and qualitative results are given. The simulations show that the proposed EMD-based method provides very good results for denoising and BW removal.     

心脑疾病对同步十二导联心电图多重分形谱的影响

对心脑疾病人群的同步十二导联ECG(心电图)进行多重分形特性分析,发现不同导联的多重分形曲线互不重叠。计算其十二导联平均的多重分形奇异强度分布范围以及分布范围的十二个导联间的离散特性,发现不同人群中存在互为交叉而有明显不同的结果。用十二导联多重分形Δα的平均值Δα及其离散度δα(取Δα的标准差)两个参量来描述其多重分形谱特征。发现健康人与心脏病人Δα接近,但δα相差较大;健康人与脑损伤患者δα接近,但Δα相差较大。预示着多重分形特性受到神经自律和心脏组织结构的自谐特性的双重控制,特征参数Δα与神经控制相对应,δΔ与心脏组织结构自谐特性的各向异性相对应。     

Electrocardiogram analysis in adult patients with sickle cell disease

This study is an analysis of the electrocardiograms (ECGs) of 87 adult patients taken during hospital admission for sickle cell disease (homozygous S). The age range was 18 to 55 years: 38 were men and 49 were women. Seventy-two percent of all patients had abnormal ECGs. Nonspecific ST-T (NS-ST-T) wave abnormalities (53 percent) and QT interval prolongation (12 percent) were frequent. Seventy-five percent of the normal ECGs occurred in women (P < .05); and 74 percent of those with left ventricular hypertrophy (LVH) were men (P < .05). Fifteen of 21 (71 percent) patients with arrhythmias had NS-ST-T abnormalities. Systemic hypertension and ECG evidence for right-sided heart disease were rare, as was the incidence of LVH by ECG.     

Detection of QRS complexes in electrocardiogram using support vector machine

This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. Two different algorithms are presented for the detection of QRS complexes. The first uses a single-lead ECG at a time for the detection of QRS complexes, while the second uses 12-lead simultaneously recorded ECG. Both algorithms have been tested on the standard CSE ECG database. A detection rate of 99.3% is achieved when tested using a single-lead ECG. This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier.     

Detection of QRS complexes in electrocardiogram using support vector machine

This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. Two different algorithms are presented for the detection of QRS complexes. The first uses a single-lead ECG at a time for the detection of QRS complexes, while the second uses 12-lead simultaneously recorded ECG. Both algorithms have been tested on the standard CSE ECG database. A detection rate of 99.3% is achieved when tested using a single-lead ECG. This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier.     

Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks

We use artificial neural networks (ANNs) to detect signs of acute myocardial infarction (AMI) in ECGs. The 12-lead ECG is decomposed into Hermite basis functions, and the resulting coefficients are used as inputs to the ANNs. Furthermore, we present a case-based method that qualitatively explains the operation of the ANNs, by showing regions of each ECG critical for ANN response. Key ingredients in this method are: (i) a cost function used to find local ECG perturbations leading to the largest possible change in ANN output and (ii) a minimization scheme for this cost function using mean field annealing. Our approach was tested on 2238 ECGs recorded at an emergency department. The obtained ROC areas for ANNs trained with the Hermite representation and standard ECG measurements were 83.4 and 84.3% (P=0.4), respectively. We believe that the proposed method has potential as a decision support system that can provide good advice for diagnosis, as well as providing the physician with insight into the reason underlying the advice.     

New approaches to the analysis of ECG data

Recently, techniques for analyzing computerized ECG data have been developed in time domain, frequency domain and micro level analysis of signals. In body surface ECG mapping, reverse problem analysis from body surface to cardiac surface was investigated, but findings were not sufficient to be clinically useful. Heart rate variability analysis from Holter ECG tapes showed the utility for determination of autonomic nervous function by time and frequency domain analysis. Late ventricular potentials recorded by Signal Averaged ECG have been beneficial for predicting malignant ventricular tachyarrhythmias, as well as delayed potentials in atrial activity. QT dispersion from 12-lead ECG has been studied as one of the indicators of ventricular instability and there was some difficulty in detecting the end points of QT intervals such as that in patients with flat T waves. The recently developed automatic analysis of the Least square fit method for detecting terminal points of T wave might be optimal compared with the other methods. Micro volt T wave alternans by spectrum frequency analysis of T wave in consecutive heart beats is a unique technique for predicting cardiac sudden death, however the mechanism remains obscure.     

冠心患者与健康人同步12导联ECG信号的李雅普诺夫指数谱比较研究

本研究首次计算了60名具有窦性心率的冠心患者(Coronary atrery disease:CAD)和60名健康老年人的同步12导联心电图信号的李雅普诺夫指数谱.发现对同一个人,从不同导联得出的Lyapunov指数是不同的,具有明显的空间分布特性.所有导联的ECG信号的最大Lyapunov指数L1均为正数,其余指数为负,心电信号表现出明显的混沌特征.同一导联相比较,冠心患者的最大Lyapunov指数L1低于健康正常人的最大Lyapunov指数L1,提示在心肌缺血的情况下,心电信号的混沌程度下降了,重构相空间中ECG信号的奇异吸引子的动力学复杂性降低了.结果表明,在估算Lyapunov指数时,有必要指明导联的位置.在Lyapunov指数谱中,最大Lyapunov指数可以将冠心患者与健康正常人区分开来,在心脏疾病诊断中具有潜在的应用价值.     

同步十二导联心电图的ST段奇异谱分析

对同步十二导联ECG(心电图)的ST段进行分析,我们发现ST段的f(α)~α奇异谱趋向于单重分形特性,面积只有它来源的ECG的f(α)~α奇异谱的一半大小。同步十二导ECG的ST段也有f(α)~α奇异谱分布特性,也有一个合理的波动范围。用冠心病人的同步十二导ST段的f(α)~α奇异谱和健康人的同步十二导ST段的奇异谱正常波动范围进行比较,发现指示出溢出正常范围的导联数目和对ECG数据进行分析的结果对比有增多的趋势,显示了用同步十二导ST段的f(α)~α奇异谱分布来诊断冠心病比用同步十二导ECG段的f(α)~α奇异谱分布来诊断冠心病更有效。     

Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms

This paper proposes a novel approach to cardiac arrhythmia recognition from electrocardiograms (ECGs). ECGs record the electrical activity of the heart and are used to diagnose many heart disorders. The numerical ECG is first temporally abstracted into series of time-stamped events. Temporal abstraction makes use of artificial neural networks to extract interesting waves and their features from the input signals. A temporal reasoner called a chronicle recogniser processes such series in order to discover temporal patterns called chronicles which can be related to cardiac arrhythmias. Generally, it is difficult to elicit an accurate set of chronicles from a doctor. Thus, we propose to learn automatically from symbolic ECG examples the chronicles discriminating the arrhythmias belonging to some specific subset. Since temporal relationships are of major importance, inductive logic programming (ILP) is the tool of choice as it enables first-order relational learning. The approach has been evaluated on real ECGs taken from the MIT-BIH database. The performance of the different modules as well as the efficiency of the whole system is presented. The results are rather good and demonstrate that integrating numerical techniques for low level perception and symbolic techniques for high level classification is very valuable.     

AR模型在远程心电诊断中的应用

在远程心电诊断中 ,计算机辅助ECG诊断通常是在接收到ECG信号、进而解压重建后进行的 ,这样便造成诊断工作的延误。本研究提出了一种基于AR模型的ECG直接分类方法 ,利用AR模型系数及其建模误差作为特征对ECG信号进行压缩 ,并采用非线性二次判别函数形式进行特征分类。通过对MIT BIH标准数据库中的NSR、APC、PVC、SVT、VT和VF各 2 0 0个样本信号进行测试 ,获得了 93.5 %～ 97.86 %的分类精度。该方法的特点是 :诊断迅速方便 ,能同时对多类ECG信号进行有效分类 ,特别适于远程诊断应用     

The Clinical Application of a PACS-Dependent 12-Lead ECG and Image Information System in E-Medicine and Telemedicine

This study presents a software technology to transform paper-based 12-lead electrocardiography (ECG) examination into (1) 12-lead ECG electronic diagnoses (e-diagnoses) and (2) mobile diagnoses (m-diagnoses) in emergency telemedicine. While Digital Imaging and Communications in Medicine (DICOM)-based images are commonly used in hospitals, the development of computerized 12-lead ECG is impeded by heterogeneous data formats of clinically used 12-lead ECG instrumentations, such as Standard Communications Protocol (SCP) ECG and Extensible Markup Language (XML) ECG. Additionally, there is no data link between clinically used 12-lead ECG instrumentations and mobile devices. To realize computerized 12-lead ECG examination procedures and ECG telemedicine, this study develops a DICOM-based 12-lead ECG information system capable of providing clinicians with medical images and waveform-based ECG diagnoses via Picture Archiving and Communication System (PACS). First, a waveform-based DICOM-ECG converter transforming clinically used SCP-ECG and XML-ECG to DICOM is applied to PACS for image- and waveform-based DICOM file manipulation. Second, a mobile Structured Query Language database communicating with PACS is installed in physicians’ mobile phones so that they can retrieve images and waveform-based ECG ubiquitously. Clinical evaluations of this system indicated the following. First, this developed PACS-dependent 12-lead ECG information system improves 12-lead ECG management and interoperability. Second, this system enables the remote physicians to perform ubiquitous 12-lead ECG and image diagnoses, which enhances the efficiency of emergency telemedicine. These findings prove the effectiveness and usefulness of the PACS-dependent 12-lead ECG information system, which can be easily adopted in telemedicine.