ML—2型心电导联转换仪的研制及其实践效果

目的：市售SYD－4228生理学学生实验系统没有配置心电图导联电缆及导联选择装置,不能进行观察心电图的实验,为此研制本心电导联转换仪。方法：设计并制作兼容网络与导联输入电缆,组装成新型的转换仪。结果：本转换仪配合SYD系统在高血钾实验中使用,满足了SYD系统进行心电图实验的需要。结论：本转换仪既适用于SYD系统,也适用于各型心电图机,可以同步对比观察不同导联的心电图。     

A training whole-heart model for simulating propagation and ECG patterns

The forward problem of electrocardiography describes the spatio-temporal source–field relationship generating the body surface potential (BSP) and, thus, the electrocardiogram (ECG). The paper presents a ventricular and atrial model for simulating cardiac de- and repolarization and the P-, QRS- and T-wave. The atria and the ventricles are coupled, so that electroanatomical function can be simulated at ones. Movement and contraction are not taken into account while an individual geometry, fibre architecture and ECG sensor arrangement including the Wilson central terminal (WCT) as common reference were considered. This in silico whole-heart model can be used for detailed investigations of the nature of the ECG for the normal beat, arrhythmias, ischemia and infarction. In addition, this model was used as a reference tool for developing and testing different electrocardiographic inverse approaches.     

Automatic estimation of the correlation dimension for the analysis of electrocardiograms

The main purpose of the present work is the definition of a fully automatic procedure for correlation dimension (D₂) estimation. In the first part, the procedure for the estimation of the correlation dimension (D₂) is proposed and tested on various types of mathematical models: chaotic (Lorenz and Henon models), periodical (sinusoidal waves) and stochastic (Gaussian and uniform noise). In all cases, accurate D₂ estimates were obtained. The procedure can detect the presence of multiple scaling regions in the correlation integral function. The connection between the presence of multiple scaling regions and multiple dynamic activities cooperating in a system is investigated through the study of composite time series. In the second part of the paper, the proposed algorithm is applied to the study of cardiac electrical activity through the analysis of electrocardiographic signals (ECG) obtained from the commercially available MIT-BIH ECG arrhythmia database. Three groups of ECG signals have been considered: the ECGs of normal subjects and ECGs of subjects with atrial fibrillation and with premature ventricular contraction. D₂ estimates are computed on single ECG intervals (static analysis) of appropriate duration, striking a balance between stationarity requisites and accurate computation requirements. In addition, D₂ temporal variability is studied by analyzing consecutive intervals of ECG tracings (dynamic analysis). The procedure reveals the presence of multiple scaling regions in many ECG signals, and the D₂ temporal variability differs in the three ECG groups considered; it is greater in the case of atrial fibrillation than in normal sinus rhythms. This study points out the importance of considering both the static and dynamic D₂ analysis for a more complete study of the system under analysis. While the static analysis visualizes the underlying heart activity, dynamic D₂ analysis insights the time evolution of the underlying system. Received: 11 April 1997 / Accepted in revised form: 19 March 1999     

Power-line interference cancellation in ECG signals

The efficiency of notchfilters and a subtraction procedure for power-line interference cancellation in electrocardiogram (ECG) signals is assessed. In contrast with the subtraction procedure, widely used digital notch filters unacceptably affect QRS complexes. The procedure eliminates interferences of variable amplitude and frequency. The frequency modulations are overcome by adaptive synchronized sampling. Initially, this is accomplished by current hardware power-line frequency measurement. Because this approach is impossible in battery-supplied and some computer-aidd devices, a software measurement of the power-line interference period is developed.     

Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform

Classification and subsequent diagnosis of cardiac arrhythmias is an important research topic in clinical practice. Confirmation of the type of arrhythmia at an early stage is critical for reducing the risk and occurrence of cardiovascular events. Nevertheless, diagnoses must be confirmed by a combination of specialist experience and electrocardiogram (ECG) examination, which can lead to delays in diagnosis. To overcome such obstacles, this study proposes an automatic ECG classification algorithm based on transfer learning and continuous wavelet transform (CWT). The transfer learning method is able to transfer the domain knowledge and features of images to a EGG, which is a one-dimensional signal when a convolutional neural network (CNN) is used for classification. Meanwhile, CWT is used to convert a one-dimensional ECG signal into a two-dimensional signal map consisting of time-frequency components. Considering that morphological features can be helpful in arrhythmia classification, eight features related to the R peak of an ECG signal are proposed. These auxiliary features are integrated with the features extracted by the CNN and then fed into the fully linked arrhythmia classification layer. The CNN developed in this study can also be used for bird activity detection. The classification experiments were performed after converting the two types of audio files containing songbird sounds and those without songbird sounds from the NIPS4Bplus bird song dataset into the Mel spectrum. Compared to the most recent methods in the same field, the classification results improved accuracy and recognition by 11.67% and 11.57%, respectively.     

Revisiting QRS Detection Methodologies for Portable,Wearable, Battery-Operated,and Wireless ECG Systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.     

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.     

Description and validation of an ECG removal procedure for EMGdi power spectrum analysis

We describe a cross-correlation procedure for removing contaminating electrocardiogram (ECG) complexes from the diaphragmatic electromyogram (EMGdi). First, the operator selects ECG templates from the EMGdi signal during expiratory intervals. Second, these templates are used to locate ECG complexes occurring during inspiratory EMGdi activity. Third, at the point of maximum correlation between the template and these ECG complexes, the template is adjusted in size and offset to "match" the ECG complex, and adjustments are determined by the linear regression coefficients. Finally, the modified template is subtracted from the EMGdi signal. To evaluate our method, we compared the power spectral density (PSD) obtained from processing EMGdi signals by our method with those obtained from the EMGdi signal in which ECG complexes had been removed by gating. Our results indicate that PSD obtained by these two different methods shows no statistically significant differences with respect to the following features: centroid frequency, median frequency, total power, standard deviation, skewness, and kurtosis.     

Exercise ECG changes in normal women

Submaximal and/or maximal exercise was carried out by 357 women without a history of cardiovascular disease, using a bicycle ergometer and/or treadmill while monitored by a bipolar ECG lead CM₅. In 40- to 60-year-old women the incidence of an ischemic ECG pattern during or after exercise ranged from 20 to 50%. Because clinical coronary disease can be expected in less than 10% of normal women followed for 16 years, most of these ECG changes were not considered to be due to occult coronary disease. At the present time exercise ECG changes in women cannot be used with any reliability as an aid in the diagnosis of chest pain or in screening normal female populations for coronary heart disease.     

单片机模拟心电图发生器的制作

本文介绍了采用８９Ｇ５１单片机的心电图信号发生器的原理和制作。本仪器小巧,灵便,能模拟产生标准件的十二导联心电图波形信号,可用来调整心电图仪和心电监视器的增益,走纸速度以及检查导联线,病人电极的性能。     

Representation of collective electrical behavior of cardiac cell sheets

The electrocardiogram (ECG) is a measure of the collective electrical behavior of the heart based on body surface measurements. With computational models or tissue preparations, various methods have been used to compute the pseudo-ECG (pECG) of bipolar and unipolar leads that can be given clinical interpretation. When spatial maps of transmembrane potential (V_m) are available, pECG can be derived from a weighted sum of the spatial gradients of V_m. The concept of a lead field can be used to define sensitivity curves for different bipolar and unipolar leads and to determine an effective operating height for the bipolar lead position for a two-dimensional sheet of heart cells. The pseudo-vectorcardiogram (pVCG) is computed from orthogonal bipolar lead voltages, which are derived in this study from optical voltage maps of cultured monolayers of cardiac cells. Rate and propagation direction for paced activity, rotation frequency for reentrant activity, direction of the common pathway for figure-eight reentry, and transitions from paced activity to reentry can all be distinguished using the pVCG. In contrast, the unipolar pECG does not clearly distinguish among many of the different types of electrical activity. We also show that pECG can be rapidly computed by two geometrically weighted sums of V_m, one that is summed over the area of the cell sheet and the other over the perimeter of the cell sheet. Our results are compared with those of an ad hoc difference method used in the past that consists of a simple difference of the sum of transmembrane potentials on one side of a tissue sheet and that of the other.     

Power-line interference elimination from ECG in case of non-multiplicity between the sampling rate and the power-line frequency

The paper deals with some aspects of the subtraction procedure, which removes the power-line interference (PLI) without affecting the components intrinsic to ECG. This procedure is based on the following principles: the interference is cancelled in linearly going ECG segments that have near to zero frequency content using moving averaging; the extracted samples are saved in a buffer and are then subtracted from the remaining parts of the signals. The accuracy of the subtraction procedure is analysed and improved in the cases of non-multiplicity between the sampling rate and the rated interference frequency. Extrapolation filters are applied over the buffer samples. Experiments with synthesised and real signals are carried out to assess the filter's stability. The results obtained show that the improved subtraction procedure removes the PL interference from ECG signals regardless of the type of multiplicity, odd or even, between the sampling rate and the power-line frequency.     

1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals

PurposeCardiovascular disease (CVD) is a leading cause of death globally. Electrocardiogram (ECG), which records the electrical activity of the heart, has been used for the diagnosis of CVD. The automated and robust detection of CVD from ECG signals plays a significant role for early and accurate clinical diagnosis. The purpose of this study is to provide automated detection of coronary artery disease (CAD) from ECG signals using capsule networks (CapsNet).MethodsDeep learning-based approaches have become increasingly popular in computer aided diagnosis systems. Capsule networks are one of the new promising approaches in the field of deep learning. In this study, we used 1D version of CapsNet for the automated detection of coronary artery disease (CAD) on two second (95,300) and five second-long (38,120) ECG segments. These segments are obtained from 40 normal and 7 CAD subjects. In the experimental studies, 5-fold cross validation technique is employed to evaluate performance of the model.ResultsThe proposed model, which is named as 1D-CADCapsNet, yielded a promising 5-fold diagnosis accuracy of 99.44% and 98.62% for two- and five-second ECG signal groups, respectively. We have obtained the highest performance results using 2 s ECG segment than the state-of-art studies reported in the literature.Conclusions1D-CADCapsNet model automatically learns the pertinent representations from raw ECG data without using any hand-crafted technique and can be used as a fast and accurate diagnostic tool to help cardiologists.     

Non-uniform shrinkage for obtaining computational start shape for in-vivo MRI-based plaque vulnerability assessment

BackgroundCritical mechanical conditions, such as stress within the structure and shear stress due to blood flow, predicted from in-vivo magnetic resonance image (MRI)-based computational simulations have shown to be potential in assessing carotid plaque vulnerability. Plaque contours obtained from in-vivo MRI are a result of a pressurized configuration due to physiological loading. However, in order to make accurate predictions, the computational model must be based on the loading-free geometry. A shrinkage procedure can be used to obtain the computational start shape.MethodIn this study, electrocardiograph (ECG)-gated MR-images of carotid plaques were obtained from 28 patients. The contours of each plaque were segmented manually. Additional to a uniform shrinkage procedure, a non-uniform shrinkage refinement procedure was used. This procedure was repeated until the pressurized lumen contour and fibrous cap thickness had the best match with the in-vivo image.ResultsCompared to the uniform shrinkage procedure, the non-uniform shrinkage significantly reduced the difference in lumen shape and in cap thickness at the thinnest site. Results indicate that uniform shrinkage would underestimate the critical stress in the structure by 20.5±10.7%.ConclusionFor slices with an irregular lumen shape (the ratio of the maximum width to the minimum width is more than 1.05), the non-uniform shrinkage procedure is needed to get an accurate stress profile for mechanics and MRI-based carotid plaque vulnerability assessment.     

便携式心电监护中实时信号处理方法研究

针对心电信号处理过程中的心电信号数字滤波、心电波形的动态显示、心电数据存储等问题,阐述了3个可用于心电信号实时处理的方法：一是运用滤波器频谱的周期性减少了滤波器系数个数,提高了运算速度,并根据卷积公式特点实现了数字滤波的实时性;二是运用基于内存虚拟屏幕技术实现心电波形动态显示,解决了屏幕闪烁和绘图不连续问题;三是采用嵌入式数据库SQLITE实现了心电数据存储。所有方法均考虑实时性要求,并已成功用于课题组开发的便携式心电监护仪,效果较为理想,具有很强的实用价值。     

Electrocardiogram Data Compression Techniques for Cardiac Healthcare Systems: A Methodological Review

Objective: Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues.Method: In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database.Results: It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures.Conclusion: This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance.     

Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram

The automatic detection of electrocardiogram (ECG) waves namely P, QRS and T-wave is important to cardiac disease diagnosis. This paper presents an application of support vector machine (SVM) as a classifier for the delineation of ECG wave components in the 12-lead ECG signal. Digital filtering techniques are used to remove power line interference and baseline wander present in the ECG signal. Gradient of the filtered ECG signal is used as a feature for the detection of QRS-complexes, P- and T-waves. The performance of the algorithm is validated using original 12-lead ECG recordings from the standard CSE ECG database. Significant detection rate is achieved. The percentage of false positive and false negative detection is low. The method successfully detects all kind of morphologies of QRS-complexes, P- and T-waves. The onsets and offsets of the detected QRS-complexes, P- and T-waves are found to be within the tolerance limits given in CSE library.     

The exercise ECG: still a useful exercise?

What is wrong with traditional ST-segment criteria during the exercise ECG? As we all know from daily practice, poor sensitivity of the exercise ECG for the detection of myocardial ischaemia is a major diagnostic weakness and a critical limitation of the exercise procedure. In standard meta-analyses, 1.0 mm (0.1 mV) of horizontal or downward-sloping ST depression has a sensitivity of only 68% for the detection of coronary artery disease (CAD), and this figure is even lower for women. This might explain our increasing reliance on noninvasive imaging modalities such as nuclear imaging (SPECT), magnetic resonance imaging (MRI), and computed tomography angiography (CTA), all of which show sensitivities between 80 and 90% for detecting CAD. As a result, there is a tendency to consider the exercise ECG as a poor man’s procedure to demonstrate myocardial ischaemia due to CAD. Is this the right consideration?     

Visualization of cardiac health using vector cardiogram

An electrocardiogram (ECG) is an electrical recording of the heart and is used in the investigation of heart disease. It displays an apparent periodicity (of about 60–100 bpm in a healthy adult), but is not exactly periodic. The symptoms of disease may show up only during certain periods of the day, and that too may occur at random in the time scale. Visual media is a most effective tool for communication, especially when the data has subtle variations. A novel visualization technique is presented to display each ECG beat. The features like PR interval, QRS width, ST interval, are extracted from the magnitude and phase plot of different lead combinations. These features are displayed on a Cartesian quadrant as different curves, with a menu driven display strategy to visualize the ECG for a chosen interval. The scheme employed can be used to identify different types of abnormalities.     

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。