Wavelet transform-based QRS complex detector

In this paper, we describe a QRS complex detector based on the dyadic wavelet transform (Dy WT) which is robust to time-varying QRS complex morphology and to noise. We design a spline wavelet that is suitable for QRS detection. The scales of this wavelet are chosen based on the spectral characteristics of the electrocardiogram (ECG) signal. We illustrate the performance of the Dy WT-based QRS detector by considering problematic ECG signals from the American Heart Association (AHA) data base. Seventy hours of data was considered. We also compare the performance of Dy WT-based QRS detector with detectors based on Okada, Hamilton-Tompkins, and multiplication of the backward difference algorithms. From the comparison, results we observed that although no one algorithm exhibited superior performance in all situations, the Dy WT-based detector compared well with the standard techniques. For multiform premature ventricular contractions, bigeminy, and couplets tapes, the Dy WT-based detector exhibited excellent performance.     

Estimation of QRS complex power spectra for design of a QRS filter

We present power spectral analysis of ECG waveforms as well as isolated QRS complexes and episodes of noise and artifact. The power spectral analysis shows that the QRS complex could be separated from other interfering signals. A bandpass filter that maximizes the signal (QRS complex)-to-noise (T-waves, 60 Hz, EMG, etc.) ratio would be of use in many ECG monitoring instruments. We calculate the coherence function and, from that, the signal-to-noise ratio. Upon carrying out this analysis on experimentaly obtained ECG data, we observe that a bandpass filter with a center frequency of 17 Hz and a Q of 5 yields the best signal-to-noise ratio.     

Redefining performance evaluation tools for real-time QRS complex classification systems

In a heartbeat classification procedure, the detection of QRS complex waveforms is necessary. In many studies, this heartbeat extraction function is not considered: the inputs of the classifier are assumed to be correctly identified. This communication aims to redefine classical performance evaluation tools in entire QRS complex classification systems and to evaluate the effects induced by QRS detection errors on the performance of heartbeat classification processing (normal versus abnormal). Performance statistics are given and discussed considering the MIT/BIH database records that are replayed on a real-time classification system composed of the classical detector proposed by Hamilton and Tompkins, followed by a neural-network classifier. This study shows that a classification accuracy of 96.72% falls to 94.90% when a drop of 1.78% error rate is introduced in the detector quality. This corresponds to an increase of about 50% bad classifications.     

Analysis of abnormal signals within the QRS complex of thehigh-resolution electrocardiogram

Presents a new, quantitative approach to measuring abnormal intra-QRS signals, using the high-resolution electrocardiogram (HRECG). These signals are conventionally known as QRS “notches and slurs.” They are measured qualitatively and form the basis for the ECG identification of myocardial infarction. The HRECG is used for detection of ventricular late potentials (LP), which are linked with the presence of a reentry substrate for ventricular tachycardia (VT) after a myocardial infarction. LP's are defined as signals from areas of delayed conduction which outlast the normal QRS period. The authors' objective is to quantify very low-level abnormal signals that may not outlast the normal QRS period. In this work, abnormal intra-QRS potentials (AIQP) were characterized by removing the predictable, smooth part of the QRS from the original waveform. This was represented as the impulse response of an ARX parametric model, with model order selected empirically from a training data set. AIQP were estimated using the residual of the modeling procedure. Critical AIQP parameters to separate VT and non-VT subjects were obtained using discriminant functions. Results suggest that AIQP indexes are a new predictive index of the HRECG for VT. The concept of abnormal intra-QRS potentials permits the characterization of pathophysiological signals contained wholly within the normal QRS period, but related to arrhythmogenesis. The new method may have other applications, such as detection of myocardial ischemia and improved ECG identification of the site of myocardial infarction, particularly in the absence of Q waves     

Modeling complex via hole structures

Derives a physics-based circuit model for complex via hole structures in printed circuit boards. The via hole is modeled as a cascade of capacitance and inductance matrices. Capacitance values are computed using a three-dimensional electrostatic solver and inductance values are computed from a two-dimensional quasi-TEM solver. This model is valid at frequencies up to a few gigahertz for typical via hole geometries, where the return current follows a well defined path.     

Modeling complexity of a complex gate oxide

Hafnium dioxide has been recently introduced as a gate dielectric in the field effect transistors. It belongs to a class of high dielectric constant or high-k dielectrics. We briefly discuss the structural and electronic properties of bulk hafnia, and show how oxygen vacancies believed to affect the band alignment across the metal oxide semiconductor (MOS) stack are stabilized in hafnia films next to high work function metals.     

复杂网络攻击建模仿真与安全评估方法

互联网是复杂网络中重要的分支,其很多的性质、功能、特性都和复杂网络的性质相关。利用复杂网络的方式建模并利用MATLAB工具,对互联网的网络攻击开展仿真研究,是研究互联网安全特性的很好切入点。基于经典的加权无标度网络模型对复杂网络攻击,本研究选定复杂网络中鲁棒性及网络效率测度来评估网络的安全性,研究和分析了网络在不同的网络环境和攻击模式下的安全性。     

基于小波变换的QRS波检测算法

针对基于小渡变换的心电信号QRS波的检测算法的计算量较大,硬件不易实现的问题,提出一种FPGA的实现方案.首先分析了利用小波变换检测QRS波群的算法,给出硬件实现方案,该算法由小波变换模块和检测模块两个模块实现.然后选取高端FPGA作为硬件处理平台,给出小波变换模块及波形检测模块具体实现结构.最后在Quartus Ⅱ下进行编译和仿真,完成心电信号检测算法的硬件实现.从综合后的资源占有率上可以看出系统充分利用了FPGA内部丰富的资源,从仿真的结果看出在FPGA系统上准确的检测出了QRS波.     

A Real-Time QRS Detection Algorithm

We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.     

战术无线电通信系统所面临的复杂电磁环境建模与仿真

复杂电磁环境是信息化战争的重要特征,运用计算机仿真技术模拟和分析战场复杂电磁环境,是建立未来高技术条件下作战仿真系统的重要基础.对无线电跳频电台的工作效能产生负面影响的电磁现象复杂多样,作者进行全面的总结和分析,做了概括和抽象,并运用matlab软件进行仿真.在此基础上,根据电磁环境分级标准,建立了复杂电磁环境仿真模型;在建立的模型中考虑到电磁现象的发生概率,以及干扰准则等因素.     

QRS feature extraction using linear prediction

This communication proposes a method called linear prediction (a high performant technique in digital speech processing) for analyzing digital ECG signals. There are several significant properties indicating that ECG signals have an important feature in the residual error signal obtained after processing by Durbin's linear prediction algorithm. This communication also indicates that the prediction order need not be more than two for fast arrhythmia detection. The ECG signal classification puts an emphasis on the residual error signal. For each ECG's QRS complex, the feature for recognition is obtained from a nonlinear transformation which transforms every residual error signal to a set of three states pulse-code train relative to the original ECG signal. The pulse-code train has the advantage of easy implementation in digital hardware circuits to achieve automated ECG diagnosis. The algorithm performs very well in feature extraction in arrhythmia detection. Using this method, our studies indicate that the PVC (premature ventricular contraction) detection has at least a 92 percent sensitivity for MIT/BIH arrhythmia database.     

Integrated approach for fetal QRS detection

Fetal magnetocardiography provides reliable signals of the fetal heart dynamics with high temporal resolution that can be used in a clinical setting. We present a robust Hilbert transform method for extraction of the fetal heart rate. Our method may be applied to signals derived from a single channel or an array of channels. In the case of multichannel data, the channels can be combined to improve signal-to-noise ratio for the extraction of fetal heart data. The method is inherently insensitive to fetal position or movement and, in addition, can be automated. We demonstrate that the determination of R-wave timing is relatively insensitive to waveform morphology. The method can also be applied if the data were preprocessed by independent component analysis (ICA). We compared the Hilbert method, ICA, ICA $+$ Hilbert, and raw signals and found that the Hilbert method gave the best overall performance. We demonstrated that there were approximately 171 errors in 46 789 fetal heart beats.      

Estimation accuracy of P wave and QRS complex occurrence times in the ECG: The accuracy for simplified theoretical and computer simulated waveforms

One of the main points of interest in the study of the dynamic behaviour of ECG time intervals is the accuracy with which characteristic moments can be estimated in the various waveform segments such as the P wave or QRS complex. In this study, the error involved in such estimation is regarded as due to the superposition of various types of disturbances (noise, hum and fluctuations in amplitude and symmetry) on a supposedly ideal ECG waveform. The effect of these disturbances on estimation accuracy is investigated for three estimation methods (peak estimation, double level estimation and matched filter estimation) by two different approaches; one based on use of a highly simplified theoretical model permitting the derivation of mathematical expressions for the estimation error, and one involving computer-aided simulation of ECG waveforms, based on real ECG data, with various types of disturbances on the basis of recorded ECG data. Both approaches indicate that noise and hum make the main contribution to estimation error, and that matched filter estimation is likely to give best estimation accuracy for both P waves and QRS complexes.     

Modeling MIMO channels using a class of complex recurrent neural network architectures

Artificial Neural Network (ANN) as non-parametric pattern mapping tool with suitable modification can tackle time varying nature of Multiple Input Multiple Output (MIMO) wireless set-up while carrying out channel modeling and estimation. Modified ANNs with temporal characteristics, however, suffer from configuration complexities. The Recurrent Neural Network (RNN), having better time tracking capability, provides a viable alternative with certain challenges. The RNN as Complex Time Delay Fully Recurrent Neural Network (CTDFRNN) block can be combined at the output using time averaging and Self Organization Map (SOM)-based optimization, yielding a new architectural framework. The CTDFRNN based designs are explored here and several such blocks are coupled together to form a cluster which generates certain diversity aspects that improves overall performance. A Modular Network SOM (MNSOM) architecture which is regarded to have certain resemblance with biological computation with an inherent reinforced modular learning, is also proposed and formulated using CTDFRNN blocks for application in MIMO channel estimation. It is found that such architectures offer considerable amount of processing time saving than the conventional stochastic estimation.     

Modeling and investigation of a geometrically complex UWB GPR antenna using FDTD

A detailed analysis of ultrawide-band (UWB), dual-polarized, dielectric-loaded horn-fed bow-tie (HFB) antennas is carried out using the finite-difference time-domain (FDTD) method. The FDTD model includes realistic features of the antenna structure such as the feeding cables, wave launchers, dielectric loading, and resistive-film loading. Important antenna characteristics that are usually difficult to obtain via measurements can be obtained more directly from this FDTD model. Since the HFB antennas under consideration are intended for ground penetrating radar (GPR) applications, the effects of the half-space medium are also investigated. The simulated results serve to verify the performance of the HFB antenna design, and to optimize various antenna parameters.     

On the detection of QRS variations in the ECG

Detection of subtle beat-to-beat variations in the morphology of the ECG is complicated by the effects of alignment errors and respiration. A method of directly estimating the alignment error (trigger jitter) from an ECG is derived by relating the variance to the squared slope of the averaged QRS complex. Results based on recordings obtained from 12 normal subjects and alignment performed by the cross-correlation method showed that the alignment errors were dependent upon the choice of the alignment channel, with the best distribution of the errors occurring when alignment was based on the vector magnitude of the three orthogonal leads. The estimated average alignment errors ranged from 0.33-0.42 ms, which were near the optimal value of 0.29 ms based on the sampling rate of 1000 samples/s. It was shown that the effects of respiration could be reduced by normalizing the amplitude of the QRS complexes. It was also estimated that a significant proportion of the variation (0.54-0.67) in the normalized ECG's could be attributed to alignment errors and noise     

复杂目标降维建模方法及其在电磁散射中的应用

任何金属目标结构都可以看作由若干相互连接的平板或弯板所组成,采用若干双线性表面面元来拟合实际目标表面,将目标表面的三维坐标降为双线性表面的二维坐标,从而使得目标表面电流的三个分量简化为双线性表面的二个分量;根据提出的混合域基函数,采用矩量法对目标模型的电磁散射特性进行分析计算.计算结果表明,此方法可以降低对计算机的内存需求,并有利于电磁散射问题的计算机快速求解.     

基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

A QRS Preprocessor Based on Digital Differentiation

Digital preprocessors can ease the increasing data collection demands placed on real-time computers in patient monitoring. This paper describes a preprocessor that detects in real time an electrocardiogram QRS complex and computes the R-R interval. Detection is performed using multiple digital differentiation, which is encouraging as an ultrareliable means for locating the QRS. Inherent in the technique is a dependable control that can automatically compensate for signal-level variations. Clinical data demonstrate that detection is insensitive to low- and high-frequency noise, from baseline drift to muscle artifact and cautery bursts. The device can be connected directly to a patient, whose safety is guaranteed by optoelectronic isolation and interelectrode current limiting. Preprocessor operation has been human-engineered to a simple on/off procedure.