Modeling the relationship between concurrent epicardial action potentials and bipolar electrograms

A signal analysis approach to building the relationship between concurrent epicardial cell action potentials (AP's) and bipolar electrograms is presented. Wavelet network, one nonlinear black-box modeling method, is used to identify the relationship between cell AP's and bipolar electrocardiograms. The electrical signals were simultaneously measured from the epicardium of isolated Langendorff-perfused rabbit hearts during three different rhythm conditions: normal sinus rhythm (NSR), normal sinus rhythm after ischemia (NSRI), and ventricular fibrillation (VF). For NSR and NSRI, the proposed modeling method successfully captures the nonlinear input-output relationship and provides an accurate output, but the method fails in case of VF. This result suggests that a time-invariant nonlinear modeling method such as wavelet network is not appropriate for VF rhythm, which is thought to be time-varying as well as chaotic, but still useful in detection of VF. A new arrhythmia detection algorithm, with potential application in implantable devices, is proposed for identifying the time of rhythmic bifurcation.     

Computer modeling of ventricular rhythm during atrial fibrillation and ventricular pacing

We propose a unified atrial fibrillation (AF)-ventricular pacing (VP) (AF-VP) model to demonstrate the effects of VP on the ventricular rhythm during atrial fibrillation AF. In this model, the AV junction (AVJ) is treated as a lumped structure characterized by refractoriness and automaticity. Bombarded by random AF impulses, the AVJ can also be invaded by the VP-induced retrograde wave. The model includes bidirectional conduction delays in the AVJ and ventricle. Both refractory period and conduction delay of the AVJ are dependent upon its recovery time. The electrotonic modulation by blocked impulses is also considered in the model. Our simulations show that, with proper parameter settings, the present model can account for most principal statistical properties of the RR intervals during AF. We further demonstrate that the AV conduction property and the ventricular rate in AF depend on both AF rate and the degree of electrotonic modulation in the AVJ. Finally, we show that multilevel interactions between AF and VP can generate various patterns of ventricular rhythm that are consistent with previous experimental observations.     

Multiway sequential hypothesis testing for tachyarrhythmiadiscrimination

A multiway sequential hypothesis testing (M-SHT) algorithm is proposed for simultaneous discrimination of cardiac tachyarrhythmias-supraventricular tachycardia (SVT) and ventricular tachycardia (VT)-from normal sinus rhythm (NSR). The M-SHT algorithm calculates a likelihood function from atrio-ventricular delay measurements, and compares this function with thresholds derived from specified error probabilities for the arrhythmias to be discriminated. Performance of this algorithm was evaluated on dual channel endocardial electrograms recorded in the cardiac electrophysiology laboratory. Two databases were developed, one for development of the algorithm and another for evaluation. The M-SHT algorithm accurately classified 26 out of 28 NSR (2 misclassified as SVT), 31 out of 31 cases of SVT, and 41 out of 43 VT (2 misclassified as NSR). The average length of time taken for classification of the three rhythms was: 3.6 s for NSR, 5.0 s for SVT, and 1.6 s for VT. Unique features of this algorithm are that acceptable error rates for each arrhythmia are independently specified and accuracy can be traded off for a faster detection time, and vice versa     

A robust sequential detection algorithm for cardiac arrhythmiaclassification

In ibid., vol. 37, no. 9, p. 837-43 (1990) and Proc. IEEE 9th Annu. Conf. Eng. Med. Biol. Soc., p. 918-19 (1988) N.V. Thakor et al. describe a sequential probability ratio test (SPRT) based on threshold crossing intervals (TCI) for the discrimination of ventricular fibrillation (VF) from ventricular tachycardia (VT). However, in applying their algorithm to data from the MIT-BIH malignant arrhythmia database, the authors observed some overlap in the distributions of TCI for VF and VT resulting in 16% overall error rate for the discrimination. In this communication, the authors describe a modified SPRT algorithm, using a new feature dubbed blanking variability (BV) as the basis for discrimination. Using the MIT-BIH database, the preliminary results showed that the proposed method decreases the overall error rate to 5%     

An atrioventricular node model for analysis of the ventricular response during atrial fibrillation

This paper introduces a model of the atrioventricular node function during atrial fibrillation (AF), and describes the related ECG-based estimation method. The proposed model is defined by parameters that characterize the arrival rate of atrial impulses, the probability of an impulse choosing either one of the two atrioventricular nodal pathways, the refractory periods of these pathways, and the prolongation of the refractory periods. These parameters are estimated from the RR intervals using maximum likelihood estimation, except for the shorter refractory period which is estimated from the RR interval Poincaré plot, and the mean arrival rate of atrial impulses by the AF frequency. Simulations indicated that 200-300 RR intervals are generally needed for the estimates to be accurate. The model was evaluated on 30-min ECG segments from 36 AF patients. The results showed that 88% of the segments can be accurately modeled when the estimated probability density function (PDF) and an empirical PDF were at least 80% in agreement. The model parameters were estimated during head-up tilt test to assess differences caused by sympathetic stimulation. Both refractory periods decreased as a result of stimulation, and the likelihood of an impulse choosing the pathway with the shorter refractory period increased.     

A detector for a chronic implantable atrial tachyarrhythmia monitor

Continuous long-term monitoring of atrial fibrillation (AF) and tachycardia (AT) is an unmet clinical need, which could be met with a chronically-implanted monitor. Improved therapeutic decisions based on accurate monitoring of parameters, such as daily AF/AT burden (hours/ day) may lead to improvements in clinical outcomes such as reduction in hospitalizations, symptoms, and strokes. This paper describes an AF/AT detector that detects AF as well as AT with an irregular ventricular response, and a supplementary AT detector for AT with more regular ventricular response. Seven databases with significant durations of AF, AT, and sinus rhythm were used to evaluate the performance of the detectors. All patient records with AF (N = 124) were detected by the AF/AT detector to have AF/AT burden with a mean, median, and 75 percentile of absolute error in burden detection of 8.8, 0, and 4 min, respectively. In patients having AF burden (= or > 10 min), the AF/AT detector was found to have burden accuracy within 20% of true burden in 96% of patients. The specificity was 94%, defined as follows: in patient records without AF/AT (N = 174), the percentage with AF/AT burden = or < 10 min in the 24-h recordings. The AF/AT detector underestimatesAT burden, thus degrading performance, in patients with significant amounts of AT with more regular ventricular response. The supplementary AT detector reduces the underestimation of AT while overestimating burden in patients without a significant amount of AT. The detectors described here could be implemented in an implantable monitor for accurate long-term AF/AT monitoring.     

Frequency spectra of ventricular tachycardia and sinus rhythm inhuman intracardiac electrograms-application to tachycardia detection forcardiac pacemakers

Presently available antitachycardia pacemakers detect ventricular tachycardia (VT) by measurement of the period of the intracardiac electrogram (EGM). The resulting inability to differentiate between VT and rapid normal sinus rhythm (NSR) can cause inappropriate pacemaker output, possibly initiating an arrhythmia where none existed previously. It is reported that FFT analysis of matched-pair NSR-VT EGM recordings from 33 patients revealed a mean (±SD) NSR-VT difference in the peak amplitude point of 8 (±7) Hz and a difference of 18 (±18) Hz in the -3 dB point. NSR-VT amplitude differences could be significantly increased by filtering over the range of greatest spectral differences. Variable-passband programmable filters could enhance VT detection in antitachycardia pacemakers     

QRS Detection Based on Morphological Filter and Energy Envelope for Applications in Body Sensor Networks

Emerging body sensor networks (BSN) provide solutions for continuous health monitoring at anytime and from anywhere. The implementation of these monitoring solutions requires wearable sensor devices and thus creates new technology challenges in both software and hardware. This paper presents a QRS detection method for wearable Electrocardiogram (ECG) sensor in body sensor networks. The success of proposed method is based on the combination of two computationally efficient procedures, i.e., single-scale mathematical morphological (MM) filter and approximated envelope. The MM filter removes baseline wandering, impulsive noise and the offset of DC component while the approximated envelope enhances the QRS complexes. The performance of the algorithm is verified with standard MIT-BIH arrhythmia database as well as exercise ECG data. It achieves a low detection error rate of 0.42% based on the MIT-BIH database.     

基于排列熵的心电信号非线性分析

排列熵是一种基于复杂性量度的非线性动力学参数，能够快速、有效地反映系统的特征。目前在心电信号检测研究中还少有此类方法的应用，通过对算法进行研究，并应用于心室病症的检测中，利用MIT—BIH数据库，对心室病症心电信号的发病时段进行排列熵值的计算，实验得出，熵值对病症发作时段的反映灵敏，在心室纤颤、室性心动过速等病症上的检测准确率较高。因此得到结论，排列熵是一个早期检测病症的灵敏参数，可以作为临床诊断的有效依据。     

自适应CA-CFAR的心电信号R波检测技术

针对心电信号R波的突变特性,利用雷达信号的检测方法,本文提出一种自适应单元平均恒虚警率(cell averaging-constant false alarm rate, CA-CFAR)的R波检测方法。首先利用滤波器组对心电信号进行预处理;然后将预处理后的信号利用自适应CA-CFAR检测判决;最后由心电信号R波的间隔特性做一个不应期剔除规则的处理,得到R波的定位。对美国麻省理工学院提供的MIT-BIH数据库中心电图(Electrocardiograph, ECG)信号仿真,实验证明,自适应参考单元的CA-CFAR对MIT-BIH的ECG信号R波检测的精准率为99.842%,检测误差为0.354%。实测数据表明了算法的有效性和适用性。      

Efficient Automatic Text Location Method and Content-Based Indexing and Structuring of Video Database

An efficient automatic text detection and location method for video documents is proposed and its application for the content-based retrieval of video is presented and discussed. Target frames are selected at fixed time intervals from shots detected by a scene-change detection method. For each selected frame, segmentation by color clustering is performed around color peaks using a color histogram. For each color plane, text-lines are detected using heuristics, and the temporal and spatial position and the text-image of each text-line are stored in a database. Experimental results for text detection in video images and the performance of the method are reported for various video documents. A user interface for text-image based browsing is designed for direct content-based access to video documents, and other applications are discussed.     

Beat-based ECG compression using gain-shape vector quantization

An electrocardiogram (ECG) data compression scheme is presented using the gain-shape vector quantization. The proposed approach utilizes the fact that ECG signals generally show redundancy among adjacent heartbeats and adjacent samples. An ECG signal is QRS detected and segmented according to the detected fiducial points. The segmented heartbeats are vector quantized, and the residual signals are calculated and encoded using the AREA algorithm. The experimental results show that with the proposed method both visual quality and the objective quality are excellent even in low bit rates. An average PRD of 5.97% at 127 b/s is obtained for the entire 48 records in the MIT-BIH database. The proposed method also outperforms others for the same test dataset.     

基于Matlab的小波提升与心电信号R波检测

本文以MIT-BIH心电数据库作为研究对象,简要介绍了使用小波变换进行心电信号检测的原理和小波提升算法的机制,阐述了采用提升小波变换的方法分解ECG信号并对R波进行定位的流程。给出了matlab示例代码。     

基于两尺度自卷积的直方图仿射不变量提取方法

提出了一种新的两尺度自卷积直方图仿射不变量。以图像二维坐标线性变换生成新图像为基点,建立两尺度自卷积变换,进而把新图像度密度函数值转化成直方图区间划分,由此推导出新的直方图仿射不变量。另外,重点分析了新不变量的参数选择范围,并详述了其计算步骤。利用经典的“fish”和“coil-100”测试数据库,对比性能优的多尺度自卷积归一化直方图仿射不变量,从时间效率、抗图像变形、抗视角变化等方面,全面验证了所提新仿射不变量的有效性。     

基于小波和扩展卡尔曼的心电检测

文中将小波变换和扩展卡尔曼滤波器相结合,利用小波变换多尺度多分辨的特点,将心电信号进行分解。然后对心电信号在各尺度上进行扩展卡尔曼滤波。最后在扩展卡尔曼滤波的输出结果上进行QRS波形检测。文中方法经MIT-BIH心电数据库检验,QRS波Se（探测灵敏度）在99.40%以上,同时,QRS＋P（正探测率）在99.39%以上,提高了心电信号检测的正确率。     

Detection of premature ventricular contraction arrhythmias in electrocardiogram signals with kernel methods

In this paper, we propose to investigate the capabilities of two kernel methods for the detection and classification of premature ventricular contractions (PVC) arrhythmias in Electrocardiogram (ECG signals). These kernel methods are the support vector machine and Gaussian process (GP). We propose to study these two classifiers with various feature representations of ECG signals, such as morphology, discrete wavelet transform, higher-order statistics, and S transform. The experimental results obtained on 48 records (i.e., 109,887 beats) of the MIT-BIH Arrhythmia database showed that for all feature representation adopted in this work, the GP detector trained only with 600 beats from PVC and Non-PVC classes can provide an overall accuracy and a sensitivity above 90 % on 20 records (i.e., 49,774 beats) and 28 records (i.e., 60,113 beats) seen and unseen, respectively, during the training phase.     

Color fringe removal in narrow color regions of digital images

Color fringe is an artifact that should be removed in a color digital image. This paper proposes a detection method of color fringe in narrow color regions. Near-saturation region (NSR) information as well as gradient magnitudes and directions of RGB color components are used to detect transition regions, in which color fringe occurs. Four gradient directions are used in order to improve the performance of color fringe detection. NSR is used to prevent excessive detection of possible color fringe regions. Then, the detected color fringe is removed using the color difference values. Experimental results show the effectiveness of the proposed color fringe removal method.     

Clustering ECG complexes using hermite functions and self-organizing maps

An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NN's are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method.     

Body Sensor Network Based Context-Aware QRS Detection

In this paper, a body sensor network (BSN) based context-aware QRS detection scheme is proposed. The algorithm uses the context information provided by the body sensor network to improve the QRS detection performance by dynamically selecting those leads with the best SNR and taking advantage of the best features of two complementary detection algorithms. The accelerometer data from the BSN are used to classify the daily activities of patients and provide context information. The classification results indicate the types of activities that were engaged in. They also indicate their corresponding intensity, which is related to the signal-to-noise ratio (SNR) of the ECG recordings. Activity intensity is first fed to the lead selector to eliminate those leads with low SNR, and then is fed to a selector to select a proper QRS detector according to the noise level. An MIT-BIH noise stress test database is used to evaluate the algorithms.     

An algorithm used for ventricular fibrillation detection without interrupting chest compression

Ventricular fibrillation (VF) is the primary arrhythmic event in the majority of patients suffering from sudden cardiac arrest. Attention has been focused on this particular rhythm since it is recognized that prompt therapy, especially electrical defibrillation, may lead to a successful outcome. However, current versions of automated external defibrillators (AEDs) mandate repetitive interruptions of chest compression for rhythm analyses since artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) preclude reliable electrocardiographic (ECG) rhythm analysis. Yet, repetitive interruptions in chest compression are detrimental to the success of defibrillation. The capability for rhythm analysis without requiring "hands-off" intervals will allow for more effective resuscitation. In this paper, a novel continuous-wavelet-transformation-based morphology consistency evaluation algorithm was developed for the detection of disorganized VF from organized sinus rhythm (SR) without interrupting the ongoing chest compression. The performance of this method was evaluated on both uncorrupted and corrupted ECG signals recorded from AEDs obtained from out-of-hospital victims of cardiac arrest. A total of 232 patients and 31,092 episodes of either VF or SR were accessed, in which 8195 episodes were corrupted by artifacts produced by chest compressions. We also compared the performance of this method with three other established algorithms, including VF filter, spectrum analysis, and complexity measurement. Even though there was a modest decrease in specificity and accuracy when chest compression artifact was present, the performance of this method was still superior to other reported methods for VF detection during uninterrupted CPR.