Design of a Three-Lead Synthetic ECG Generator Using the Simplified McSharry's Model

Abstract

Recently, due to the dramatic improvement in computing capabilities and processing speed of microcontrollers and digital signal processors, many analytic algorithms for electrocardiograms (ECGs) have been developed and applied to a variety of portable electronic devices. In order to test whether the algorithms can analyze the various morphologies of electrocardiogram well, it is necessary to expend much effort collecting various ECG samples to establish an ECG database. Therefore, this paper simplifies and adopts a synthetic electrocardiogram model composed of three coupled differential equations proposed by Dr. McSharry et al. (2003), as well as utilizes an improved fourth-order Taylor series to quickly approximate the exponential function within the differential equation of a simplified synthetic ECG model so that, when studying ECG analytic algorithms, the graphical user interface (GUI) application “Synthetic Waveforms Control Panel,” developed by the author using LabVIEW, can be used to attain the desirable morphology of electrocardiogram as well as the time intervals between peaks (P, Q, R, S, T) and heart rate. The parameters of the adjusted synthetic ECG model are then sent to the memory of a synthetic ECG generator through a USB interface, and the ECG generator will generate three synthetic electrocardiograms of Einthoven's triangle for testing the electrocardiogram analytic algorithms.     

经母体腹部提取胎儿心率信号的系统设计

通过电极直接从孕妇腹部检测胎儿心率信号是一种理想的无创检测方法,本文采用TI公司的TMS320C6713和专用心电检测的SOC芯片ADS1298为核心完成孕妇腹部混合心电信号检测系统的设计,使用独立分量分析方法从孕妇腹部提取的三路心电信号中分离出胎儿心率信号。实验结果表明,该系统能可靠地检测孕妇混合心电信号,并能准确提取胎儿心率。该系统具有便携、低功耗特性,适合长程动态对胎儿进行监护,为围产期孕妇子宫内胎儿的健康状况提供可靠依据,提高围产期母婴保健水平。     

Comparison of heart rate variability (HRV) and nasal pressure in obstructive sleep apnea (OSA) patients during sleep apnea

In this study, a novel R wave detection algorithm was developed and used to analyze the heart rate variability (HRV) of obstructive sleep apnea patients with obstructive sleep apnea (OSA). The purpose of our study was to investigate the biosignal changes in the synchronization between HRV, nasal pressure, and the effect of OSA. HRV, nasal pressure, and sleep electroencephalogram (EEG) signals recorded in control and OSA patients with sleep apnea who were matched according to EEG arousal in OSA during sleep apnea. Experiment steps were completed for R–R interval calculation and to estimate its power spectral density (PSD) over several frequency ranges of apnea states (severe, moderate and mild). Patients with severe OSA had persistently longer R–R intervals compared to patients with mild OSA. As a measure of apnea classification accuracy, the algorithm correctly classified 99.7% of the evaluation database. An advantage of the proposed method is the combination of R wave detection techniques to enhance the accuracy of wave detection that is easily implemented with HRV verified by accurate classification and quantification.     

抗电磁干扰的心电信号监测系统

为了实现电磁干扰环境下的心电信号监测,提出了一种抗电磁干扰的心电信号监测系统。论文给出了输出光电流的计算公式,分析了半波电压及插入损耗对系统灵敏度的影响。利用铌酸锂电光晶体搭建实验系统,测试了5位健康志愿者的心电信号。应用提出的系统和电学心电信号采集系统分别测试了正常环境及电磁环境下志愿者的心电信号。测试结果表明:在正常环境下,本系统能获得与电学心电信号采集系统同样清晰的心电信号波形;在电磁环境下,本系统获得的心电信号优于电学心电信号采集系统。最后定量计算了两者的信噪比,计算结果表明:在电磁干扰下,本系统的信噪比变化量为0.54dB(V2)/0.49dB(V4),而电学心电信号采集系统的信噪比变化量为24.07dB(V2)/16.75dB(V4)。     

一种新型心肌收缩力无创检测和评估系统的研制

研制了基于PC机视窗平台、Delphi5．0软件开发工具编程，心音传感器、心音信号调理电路及ISA扩展槽电路的新型心肌收缩力检测和评估系统。提出了一种用小波变换对心音信号进行去噪的实用方法。通过临床试验表明，该系统能无创、快速、价廉和客观量化的对心血管病人和健康人的心肌收缩力进行检测和评估。     

心电监测系统的设计

心电监测系统是用来采集、分析及显示心电信号的医疗设备。心电监测系统的硬件电路设计包括高频干扰滤除电路、导联选择电路、差动输入电路、50Hz工频干扰滤除电路、前置放大电路等模拟预处理电路的设计和基于单片机的数据采集、存储和传送系统的设计。心电监测系统的软件设计,主要是指在Visual C++6.0的开发环境下心电分析、显示软件的设计,包括患者信息输入模块、串口通讯模块、分析显示心电波形模块、保存心电数据模块和读取心电数据模块的设计。     

Detection of ECG characteristic points using Multiresolution Wavelet Analysis based Selective Coefficient Method

Automatic extraction of time plane features is important for cardiac disease diagnosis. This paper presents a multiresolution wavelet transform based system for detection and evaluation of QRS complex, P and T waves. Selective coefficient method is based on identification of proper and optimum set of wavelet coefficients to reconstruct a wave or complex of interest from the ECG signal. The performance of the system is validated using original 12 lead ECG recording collected from the physionet PTB diagnostic database. The measured values are compared with the manually determined values and measurement accuracy is calculated. The test result shows over 99% true detection rate for R peak and base accuracy over 97%, 96%, 95%, 98% for heart rate, P wave, QRS complex and T wave respectively.     

A fractal-dimension-based signal-processing technique and its use for nondestructive testing

A fractal-dimension-based signal-processing technique has been extensively applied to various fields, but the use of the method to characterize discrete time-domain ultrasonic signals reflecting defects and any other structural-material inhomogeneities has not been fully investigated. The fractal features of the ultrasonic echoes with fractal dimensions and their implementation in nondestructive testing are investigated. In order to obtain a faithful representation of the fractal dimensions, two improved fractal dimension algorithms are presented: the box-counting method and the R/S (range/standard deviation) method. Their capabilities are evaluated with two kinds of fractal signals: the FBM (fractal Brownian motion) and WM (Weierstrass-Mandelbrot) signals. A new method to guarantee the feasibility of the calculated fractal dimensions is proposed on the basis of the analysis of the results simulated above. Then, the fractal dimensions of ultrasonic signals measured from a pipeline sample and from carbon-steel and aluminum specimens are calculated and statistically analyzed to find the fractal properties of the ultrasonic signals. The experimental results show that ultrasonic signals have the property of scale invariance that the fractal set possessed. The fractal dimension is indicative of the complexity and degree of irregularity of the waveform of an ultrasonic signal. The fractal dimensions of ultrasonic signals from various defects and microstructures are found to possess solid distribution intervals, which can be used to identify the presence of defects and the features of materials. The potential of the technique for testing defects and assessing the microstructure of materials via the use of ultrasonic echoes is revealed. The text was submitted by the authors in English.     

可穿戴心电监护设备设计与实现

为了满足心脏病患者日益增长的心电监护需求,设计了一种可穿戴式的心电监护设备来满足患者日常生活的心电监护功能。设备设计包括硬件和软件2部分,硬件方面采用柔性织物电极、AD8232芯片和STM32微控制器实现对心电信号的采集、A/D转换和传输功能;软件方面借用小波变换在心电信号去噪和识别方面的优势,获得清晰的心电信号和心率,并实现对症状的初步诊断;最后设计了基于LabVIEW平台的友好人机交互界面,可以直观、清楚的显示心电信号的实时波形,当前患者的R-R间期和心率参数。利用本设备对几位志愿者进行测试,得到了良好的结果,可以实现患者日常心电监护。     

Novel cyclostationarity-based blind source separation algorithm using second order statistical properties: Theory and application to the bearing defect diagnosis

Among signal processing techniques, blind source separation (BSS) and the underlying mathematical tool of independent component analysis (ICA) are of continuously growing interest in the scientific community of various research domains. Vibration analysis is a potential application field of this quite recent technique.Actually, BSS methods aim to retrieve unknown source signals from a set of their observations coming to a matrix of sensors, without necessarily having any prior knowledge about the sources. In monitoring and diagnosis purposes, bearing defects constitute a problem for manufacturers who aim at predicting those faults as well as potential engines breakdowns. These defects may be the unknown sources one wants to estimate from a set of recorded signals by a matrix of accelerometers placed close to the rotating machine.It has been shown that these vibration signals are wide-sense cyclostationary [[11] R.B.Randall, J. Antoni, S. Chobsaard, The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals, Mechanical Systems and Signal Processing 15 (5) (2001) 945–962]. The new algorithm of BSS proposed in this work is based, precisely, on that property. Second-order statistics of such processes led us to a new separation criterion for blind source separation. The theoretical results of this study, simulation and experimental analysis are presented in here. Perspectives for future research conclude this paper.     

A novel fuzzy c-means method for classifying heartbeat cases from ECG signals

This study proposes a simple and reliable method termed the fuzzy c-means method for classifying the heartbeat cases from electrocardiogram (ECG) signals. The proposed method has the advantages of good detection results, no complex mathematic computations, low memory space and low time complexity. The FCMM can accurately classify and distinguish the difference between normal heartbeats and abnormal heartbeats. Classifying the heartbeat cases from ECG signals consists of four main procedures: (i) Procedure-DOM for detecting QRS waveform using the Difference Operation Method; (ii) qualitative features stage (Procedure-ROM) for qualitative feature selection using the Range-Overlaps Method on ECG signals; (iii) Procedure-CCC is used to compute the cluster center for each class; and (iv) Procedure-HCD is used to determine the heartbeat case for the patient. The experiments show that the sensitivities were 98.28%, 90.35%, 86.97%, 92.19%, and 94.86% for NORM, LBBB, RBBB, VPC and APC, respectively. The total classification accuracy was approximately 93.57%.     

Characteristics of elastic waves generated by fatigue crack penetration and growth in an aluminum plate

The characteristics of elastic waves emanated from crack initiation in 6061 aluminum alloy subjected to fatigue loading are investigated through experiments. The objective of the study is to determine the differences in the properties of the signals generated from fatigue test and also to examine if the sources of the waves could be identified from the temporal and spectral characteristics of the acoustic emission (AE) waveforms. The signals are recorded using nonresonant, flat, broadband transducers attached to the surface of the alloy specimens. The time dependence and power spectra of the signals recorded during the tests were examined and classified according to their special features. Six distinct types of signals were observed. The waveforms and their power spectra were found to be dependent on the crack propagation stage and the type of fracture associated with the signals. The potential application of the approach in health monitoring of structural components using a network of surface mounted broadband sensors is discussed.     

The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation

This paper describes a tool-wear monitoring procedure in a metal turning operation using vibration features. Machining of EN24 was carried out using coated grooved inserts, and on-line vibration signals were obtained. The measured tool-wear forms were correlated to features in the vibration signals in the time and frequency domains. Analysis of the results suggested that the vibration signals’ features were effective for use in cutting tool-wear monitoring and wear qualification.     

A three-lead, programmable, and microcontroller-based electrocardiogram generator with frequency domain characteristics of heart rate variability

The objective of this study is to design and develop a programmable electrocardiogram (ECG) generator with frequency domain characteristics of heart rate variability (HRV) which can be used to test the efficiency of ECG algorithms and to calibrate and maintain ECG equipment. We simplified and modified the three coupled ordinary differential equations in McSharry's model to a single differential equation to obtain the ECG signal. This system not only allows the signal amplitude, heart rate, QRS-complex slopes, and P- and T-wave position parameters to be adjusted, but can also be used to adjust the very low frequency, low frequency, and high frequency components of HRV frequency domain characteristics. The system can be tuned to function with HRV or not. When the HRV function is on, the average heart rate can be set to a value ranging from 20 to 122 beats per minute (BPM) with an adjustable variation of 1 BPM. When the HRV function is off, the heart rate can be set to a value ranging from 20 to 139 BPM with an adjustable variation of 1 BPM. The amplitude of the ECG signal can be set from 0.0 to 330 mV at a resolution of 0.005 mV. These parameters can be adjusted either via input through a keyboard or through a graphical user interface (GUI) control panel that was developed using LABVIEW. The GUI control panel depicts a preview of the ECG signal such that the user can adjust the parameters to establish a desired ECG morphology. A complete set of parameters can be stored in the flash memory of the system via a USB 2.0 interface. Our system can generate three different types of synthetic ECG signals for testing the efficiency of an ECG algorithm or calibrating and maintaining ECG equipment.     

ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier

This paper deals with ECG signal analysis based on Artificial Neural Network and combined based (discrete wavelet transform and morphology) features. We proposed a technique to truthfully classify ECG signal data into two classes (abnormal and normal class) using various neural classifier. MIT–BIH arrhythmia database utilized and selected 45 files of one minute recording (25 files of normal class and 20 files of abnormal class) out of 48 files based on types of beat present in it. The total 64 features are separated in to two classes that is DWT (48) based features and morphological (16) feature of ECG signal which is set as an input to the classifier. Three neural network classifiers: Back Propagation Network (BPN), Feed Forward Network (FFN) and Multilayered Perceptron (MLP) are employed to classify the ECG signal. The classifier performance is measured in terms of Sensitivity (Se), Positive Predictivity (PP) and Specificity (SP). The system performance is achieved with 100% accuracy using MLP.     

Source location of acoustic emission in diesel engines

This paper is the third in a series developing methods of mapping acoustic emission (AE) signals and wave propagation in engines and focuses on source location techniques for the multi-source signals on relatively complex structures typical of machinery applications. Two source location techniques, a traditional wave velocity-based and an AE energy-based technique, using triangular sensor arrays, are used to locate source positions on the cylinder head of a 74 kW diesel engine using simulated sources (pencil lead break) and real sources (e.g. injectors (INJs) and exhaust valves during engine running).Source location using both techniques is demonstrated on the cylinder head of a 74 kW four-stroke diesel engine. The velocity-based technique uses AE wave speeds and time-of-flight (wave arrival time) to locate source position and is found to be most effective for single source signals with a sharp rising edge and good signal to noise ratios. The energy-based technique is based on a simple absorption attenuation model and was found to be useful for multiple source signals such as INJ signals, although structure-specific attenuation coefficients need to be measured for accurate source location.     

Adaptive filtering techniques for velocity estimation of tomographic electric capacitance signals of two-phase gas/oil flows

The development of adaptive real-time flow velocity estimation algorithms for two-phase flows can contribute to monitoring the pipelines of various complex processes, such as energy, chemical, petroleum and nuclear industries. Among the different non-invasive tomography techniques, electrical capacitance tomography (ECT) is gaining increasing attention for its potential use in real-time imaging and characterization of multiphase flow systems. The nature of ECT signals for two-phase flows can significantly degrade the velocity estimation process with cross-correlation approaches. We address the unique challenges of such signals and propose a preprocessing technique to improve the performance and robustness of the velocity estimation algorithm. Two adaptive filters are used to estimate the velocity of a two-phase type flow. A least mean square (LMS) and a fast block LMS (FBLMS) are used to model the time delay between the two signals captured by the twin sensor (ECT). Performance of the proposed technique is assessed by applying it to ECT data obtained from an experimental flow rig. The computed estimates are then compared with the calculated velocity from tracking motion of bubbles captured by a high speed camera monitoring the two phase flow in the pipe. Results show that the proposed technique provides consistent results across various flow patterns, and is advantageous compared to cross-correlation based techniques, specially for chaotic flow conditions. Furthermore, the proposed estimation algorithms can be applied to other electric based tomographic techniques.     

Indirect monitoring of the dynamics at the contact interfaces of a pair of abrading titanium specimens

The novel method of using continuous tribo-electrification variations to monitor the dynamic tribological properties between metal films has been applied successfully [Y.P. Chang, A novel method of using continuous tribo-electrification variations for monitoring the tribological properties between the pure metal films, Wear, 262 (2007) 411–423]. The method was shown to produce clear and strong signals, superior to monitoring continuous friction coefficient variations. However, the above method was only shown to be suitable for the tested material pairs that were studied. In this paper, the method was improved and applied to monitoring the dynamic tribological properties between titanium oxide (TiO₂) films in the friction process. The experiment was conducted on a purposed-designed friction tester with a suitable measuring system. In order to investigate the tribological property of titanium (Ti) sliding against Ti with TiO₂ films in detail, the continuous variations of electrical contact resistance and friction coefficient were measured for monitoring the onset of film rupture between the TiO₂ films and the chemical reactions between the interfaces. Wear loss was measured by an accuracy balance and scan electron microscopy was used to observe the microstructures and material transfer. The experiments demonstrated that the novel method of using electrical contact resistance variations has great potential for monitoring the dynamic tribological properties and the chemical reactions of titanium specimens.     

Blind source separation of internal combustion engine piston slap from other measured vibration signals

Internal combustion engines have several vibration sources, such as combustion, fuel injection, piston slap and valve operation. For machine condition monitoring or design improvement purposes, it is necessary to separate the vibration signals caused by different sources and then analyse each of them individually. However, traditional frequency analysis techniques are not very useful due to overlap of the different sources over a wide frequency range. This paper attempts to separate the vibration sources, especially piston slap, by using blind source separation techniques with the intention of revealing the potential of the new technique for solving mechanical vibration problems. The BSS method and the Blind least mean square algorithm using Gray's variable norm as a measure of non-Gaussianity of the sources is briefly described and separation results for both simulated and measured data are presented and discussed.