Ventricular tachycardia and fibrillation detection by a sequentialhypothesis testing algorithm

An algorithm for detecting ventricular fibrillation (VF) and ventricular tachycardia (VT) by the method of sequential hypothesis testing is presented. The algorithm first generates a binary sequence by comparing the signal to a threshold. The probability distribution of the time intervals of the binary sequence is obtained, and Wald's sequential hypothesis testing procedure is next employed to discriminate the arrhythmias. Sequential hypothesis testing of 85 cases resulted in identification of 1) 97.64% VF and 97.65% VT episodes after 5 s, and 2) 100% identification of both VF and VT after 7 s. The desired false positive and false negative error probabilities can be preprogrammed into the algorithm. An important feature of the sequential method is that extra time for detection can be traded off for improved accuracy, and vice versa.     

A two-stage discrimination of cardiac arrhythmias using a total least squares-based prony modeling algorithm

In this paper, we describe a new approach for the discrimination among ventricular fibrillation (VF), ventricular tachycardia (VT) and superventricular tachycardia (SVT) developed using a total least squares (TLS)-based Prony modeling algorithm. Two features, dubbed energy fractional factor (EFF) and predominant frequency (PF), are both derived from the TLS-based Prony model. In general, EFF is adopted for discriminating SVT from ventricular tachyarrhythmias (i.e., VF and VT) first, and PF is then used for further separation of VF and VT. Overall classification is achieved by performing a two-stage process to the indicators defined by EFF and PF values, respectively. Tests conducted using 91 episodes drawn from the MIT-BIH database produced optimal predictive accuracy of (SVT, VF, VT) = (95.24%, 96.00%, 97.78%). A data decimation process is also introduced in the novel method to enhance the computational efficiency, resulting in a significant reduction in the time required for generating the feature values.     

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%     

Machine learning approach to recognize ventricular arrhythmias using VMD based features

The occurrence of life-threatening ventricular arrhythmias (VAs) such as Ventricular tachycardia (VT) and Ventricular fibrillation (VF) leads to sudden cardiac death which requires detection at an early stage. The main aim of this work is to develop an automated system using machine learning tool for accurate prediction of VAs that may reduce the mortality rate. In this paper, a novel method using variational mode decomposition (VMD) based features and C4.5 classifier for detection of ventricular arrhythmias is presented. The VMD model was used to decompose the electrocardiography (ECG) signals to extract useful informative features. The method was tested for ECG signals obtained from PhysioNet database. Two standard databases i.e. CUDB (Creighton University Ventricular Tachyarrhythmia Database) and VFDB (MIT-BIH Malignant Ventricular Ectopy Database) were considered for this work. A set of time–frequency features were extracted and ranked by the gain ratio attribute evaluation method. The ranked features are subjected to support vector machine (SVM) and C4.5 classifier for classification of normal, VT and VF classes. The best detection was obtained with sensitivity of 97.97%, specificity of 99.15%, and accuracy of 99.18% for C4.5 classifier with a 5 s data analysis window. These results were better than SVM classifier result having an average accuracy of 86.87%. Hence, the proposed method demonstrates the efficiency in detecting the life-threatening VAs and can serve as an assistive tool to clinicians in the diagnosis process.

     

Variability of the QRS signal in high-resolution electrocardiograms and magnetocardiograms

The variability of electric and magnetic signals from the heart during the depolarization phase is investigated. A signal processing method is developed, which provides estimates for the beat-to-beat variability of the QRS-complex. The method is based on the decomposition of the depolarization signal into bandpass signals by means of the Morlet wavelet transform. The beat variability of the depolarization signal is estimated by normalized variances of the envelope and instantaneous frequency of bandpass signals. Time intervals of the bandpass filtered depolarization signals having a high signal-to-noise ratio are selected applying an analysis based on phase statistics. The method was tested by computer simulation and experimental data taken from electrocardiographic and magnetocardiographic measurements of healthy persons and patients prone to malignant ventricular tachycardia (VT) or ventricular fibrillation (VF). Results suggest that the calculated variance parameters permit the characterization of beat variable depolarization signals and distinguish VT/VF patients from healthy persons.     

A low-complexity intracardiac electrogram compression algorithm

Implantable cardioverter defibrillators (ICD's) detect, diagnose and treat the potentially fatal heart arrhythmias known as bradycardia, ventricular tachycardia (VT), and ventricular fibrillation (VF) in cases where these arrhythmias are resistant to surgical and drug-based treatments by direct sensing and electrical stimulation of the heart muscle. Since the ICD is implanted, power consumption, reliability, and size are severe design constraints. This paper targets the problems associated with increasing the signal recording capabilities of an ICD. A data-compression algorithm is described which has been optimized for low power consumption and high reliability implementation. Reliance on a patient's morphology or that of a population of patients is avoided by adapting to the intracardiac electrogram (ICEG) amplitude and phase variations and by using adaptive scalar quantization. The algorithm is compared to alternative compression algorithms which are also patient independent using a subset of VT arrhythmias from a data base of 146 patients. At low distortion the algorithm is closest to the Shannon lower bound achieving an average of 3.5 b/sample at 5% root mean square distortion for a 250-Hz sample rate. At higher distortion vector quantization and Karhunen-Loeve Transform approaches are superior but at the cost of considerable additional computational complexity     

Integration of absolute ventricular fibrillation voltage correlateswith successful defibrillation

Previous work has suggested that at higher absolute ventricular fibrillation voltages (AVFV), the heart is more amenable to defibrillation. This study investigated in a canine model whether voltage integration of the AVFV is associated with the defibrillation success rate. The moving-average filter was used to process the ventricular fibrillation (VF) waveform recorded from Lead II of the electrocardiogram (ECG). In seven animals, defibrillation trials were analyzed using a DC shock (DCS) successful approximately 50% of the time when delivered randomly. For each of a total of 84 DCS (40% successes, 60% failures), the fibrillation waveform just prior to DCS was analyzed. The integration of the AVPV waveform was performed over various sample sizes including 1, 4, 8, 16, 64, and 128 ms, as well as the time equal to the mean VF cycle length. The results suggest that dc shocks delivered at instants of higher values of integrated AVFV over the various window sizes are associated with successful defibrillation. Window sizes less than 16 ms appeared to offer the best discrimination. The integration of AVFV over the entire VF cycle length was significantly higher for successful rather than unsuccessful DCS. This interesting observation is consistent with the clinical observation that “coarse” VF (high AVFV) is easier to defibrillate than “fine” VF (low AVFV). The use of voltage integration of AVFV may have potential implications in the improvement of defibrillation success in implantable devices     

A comparison of four new time-domain techniques for discriminatingmonomorphic ventricular tachycardia from sinus rhythm using ventricularwaveform morphology

Electrical management of intractable tachycardia via implantable antitachycardia devices has become a major form of therapy. Newly advanced methods of ventricular tachycardia detection propose examination of changes in intraventricular electrogram morphology in addition to or in combination with earlier rate-based detection algorithms. Unfortunately, most of the proposed morphology analysis techniques have computational demands beyond the capabilities of present devices or may be adversely affected by amplitude and baseline fluctuations of the intraventricular electrogram. We have designed four new computationally efficient time-domain algorithms for distinguishing ventricular electrograms during monomorphic ventricular tachycardia (VT) from those during sinus rhythm using direct analysis of the ventricular electrogram morphology. All four techniques are independent of amplitude fluctuations and three of the four are independent of baseline changes. These new techniques were compared to correlation waveform analysis, a previously proposed method for distinction of VT from sinus rhythm. Evaluation of these four new algorithms was performed on data from 19 consecutive patients with 31 distinct monomorphic ventricular tachycardia morphologies. Three of the algorithms performed as well or better than correlation waveform analysis but with one-tenth to one-half the computational demands.     

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.     

Universal lossless compression via multilevel pattern matching

A universal lossless data compression code called the multilevel pattern matching code (MPM code) is introduced. In processing a finite-alphabet data string of length n, the MPM code operates at O(log log n) levels sequentially. At each level, the MPM code detects matching patterns in the input data string (substrings of the data appearing in two or more nonoverlapping positions). The matching patterns detected at each level are of a fixed length which decreases by a constant factor from level to level, until this fixed length becomes one at the final level. The MPM code represents information about the matching patterns at each level as a string of tokens, with each token string encoded by an arithmetic encoder. From the concatenated encoded token strings, the decoder can reconstruct the data string via several rounds of parallel substitutions. A O(1/log n) maximal redundancy/sample upper bound is established for the MPM code with respect to any class of finite state sources of uniformly bounded complexity. We also show that the MPM code is of linear complexity in terms of time and space requirements. The results of some MPM code compression experiments are reported     

Variation in the dominant period during ventricular fibrillation

Time-varying periodicities are commonly observed in biological time series. In this paper, we discuss three different algorithms to detect and quantify change in periodicity. Each technique uses a sliding window to estimate periodic components in short subseries of a longer recording. The three techniques we utilize are based on: 1) standard Fourier spectral estimation; 2) an information theoretic adaption of linear (autoregressive) modeling; and 3) geometric properties of the embedded time series. We compare the results obtained from each of these methods using artificial data and experimental data from swine ventricular fibrillation (VF). Spectral estimates have previously been applied to VF time series to show a time-dependent trend in the dominant frequency. We confirm this result by showing that the dominant period of VF, following onset, first decreases to a minimum and then rises to a plateau. Furthermore, our algorithms detect longer period correlations which may indicate the presence of additional periodic oscillations or more complex nonlinear structure. We show that in general this possibly nonlinear structure is most apparent immediately after the onset of VF.     

Taser dart-to-heart distance that causes ventricular fibrillation in pigs

Electromuscular incapacitating devices (EMDs), such as Tasers, deliver high current, short duration pulses that cause muscular contractions and temporarily incapacitate the human subject. Some reports suggest that EMDs can kill. To help answer the question, "Can the EMD directly cause ventricular fibrillation (VF)?", ten tests were conducted to measure the dart-to-heart distance that causes VF in anesthetized pigs [mass = 64 kg +/- 6.67 standard deviation (SD)] for the most common X26 Taser. The dart-to-heart distance that caused VF was 17 mm +/- 6.48 (SD) for the first VF event and 13.7 mm +/- 6.79 (SD) for the average of the successive VF events. The result shows that when the stimulation dart is close enough to the heart, X26 Taser current will directly trigger VF in pigs. Echocardiography of erect humans shows skin-to-heart distances from 10 to 57 mm (dart-to-heart distances of 1-48 mm). These results suggest that the probability of a dart on the body landing in 1 cm2 over the ventricle and causing VF is 0.000172.     

Predicting patterns of epicardial potentials during ventricularfibrillation

Ventricular fibrillation (VF) is a fatal cardiac arrhythmia, characterized by uncoordinated propagation of activation wavefronts in the ventricular myocardium. Short-term predictions of epicardial potential fields during VF in pigs were attempted using linear techniques, and prediction accuracy was measured at various stages during sustained episodes. VF was induced in five pigs via premature electrical stimulation. Unipolar electrograms were recorded from an epicardial array of 506 electrodes in a 22×23 array with 1-mm spacing. Optimal spatial basis functions (modes) and time-varying weighting coefficients were found using the Karhunen-Loeve decomposition. Linear autoregressive (AR) models incorporating the dynamics of only a few spatial modes led to predicted patterns that were qualitatively similar to observed patterns. Predictions were made 0.256 s into the future, based on 0.768 s of past data, over an area of approximately 5 cm² on the ventricular epicardium. The mean squared error of predictions varied from as much as 1.23 to as little as 0.14, normalized to the variance of the actual data. Inconsistency in long-term forecasts is partly due to the limitations of linear AR models. Changes in predictability, however, were consistent. Predictability varied inversely with spatial complexity, as measured by the mean squared error of a five-mode approximation. Predictability also increased significantly during the first minute of VF     

Correspondence between simple 3-D MRI-based computer models and in-vivo EP measurements in swine with chronic infarctions

The aim of this paper was to compare several in-vivo electrophysiological (EP) characteristics measured in a swine model of chronic infarct, with those predicted by simple 3-D MRI-based computer models built from ex-vivo scans (voxel size <1 mm(3)). Specifically, we recorded electroanatomical voltage maps (EAVM) in six animals, and ECG waves during induction of arrhythmia in two of these cases. The infarct heterogeneities (dense scar and border zone) as well as fiber directions were estimated using diffusion weighted DW-MRI. We found a good correspondence (r = 0.9) between scar areas delineated on the EAVM and MRI maps. For theoretical predictions, we used a simple two-variable macroscopic model and computed the propagation of action potential after application of a train of stimuli, with location and timing replicating the stimulation protocol used in the in-vivo EP study. Simulation results are exemplified for two hearts: one with noninducible ventricular tachycardia (VT), and another with a macroreentrant VT (for the latter, the average predicted VT cycle length was 273 ms, compared to a recorded VT of 250 ms).     

TSP的DNA算法

由于Adleman和Lipton的开创性工作,最近DNA计算引起了人们的极大兴趣,他们提出的分子算法解决了图形的表示方法,但是没有给出如何处理图中节点的弧线的信息。本文的目的是通过提出在图中城市间的距离用简单的弧线代表,延伸了Adleman和Lipton提出的基本的分子算法。并提出只有当算法步骤由当前的需要人工干预被可执行的可在试管中操作的DNA链代替,解决计算难题的真正可行DNA计算可以实现。该算法的创新之处在于表示城市和路径的DNA链长度的设计,能使我们在合理的范围内寻找旅行商问题的解,较大地简化了问题的复杂度。     

A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques

Ventricular tachyarrhythmias, in particular ventricular fibrillation (VF), are the primary arrhythmic events in the majority of patients suffering from sudden cardiac death. Attention has focused upon these articular rhythms as it is recognized that prompt therapy can lead to a successful outcome. There has been considerable interest in analysis of the surface electrocardiogram (ECG) in VF centred on attempts to understand the pathophysiological processes occurring in sudden cardiac death, predicting the efficacy of therapy, and guiding the use of alternative or adjunct therapies to improve resuscitation success rates. Atrial fibrillation (AF) and ventricular tachycardia (VT) are other types of tachyarrhythmias that constitute a medical challenge. In this paper, a high order spectral analysis technique is suggested for quantitative analysis and classification of cardiac arrhythmias. The algorithm is based upon bispectral analysis techniques. The bispectrum is estimated using an autoregressive model, and the frequency support of the bispectrum is extracted as a quantitative measure to classify atrial and ventricular tachyarrhythmias. Results show a significant difference in the parameter values for different arrhythmias. Moreover, the bicoherency spectrum shows different bicoherency values for normal and tachycardia patients. In particular, the bicoherency indicates that phase coupling decreases as arrhythmia kicks in. The simplicity of the classification parameter and the obtained specificity and sensitivity of the classification scheme reveal the importance of higher order spectral analysis in the classification of life threatening arrhythmias. Further investigations and modification of the classification scheme could inherently improve the results of this technique and predict the instant of arrhythmia change.     

Efficient time-recursive implementation of matched filterbank spectral estimators

In this paper, we present a computationally efficient sliding window time updating of the Capon and amplitude and phase estimation (APES) matched filterbank spectral estimators based on the time-variant displacement structure of the data covariance matrix. The presented algorithm forms a natural extension of the most computationally efficient algorithm to date, and offers a significant computational gain as compared to the computational complexity associated with the batch re-evaluation of the spectral estimates for each time-update. Furthermore, through simulations, the algorithm is found to be numerically superior to the time-updated spectral estimate formed from directly updating the data covariance matrix.     

Complexity of strings in the class of Markov sources

Shannon's self-information of a string is generalized to its complexity relative to the class of finite-state-machine (FSM) defined sources. Unlike an earlier generalization, the new one is valid for both short and long strings. The definition is justified in part by a theorem stating that, asymptotically, the mean complexity provides a tight lower bound for the mean length of all so-called regular codes. This also generalizes Shannon's noiseless coding theorem. For a large subclass of FSM sources a simple algorithm is described for computing the complexity.     

用光谱辐射计测量大气光学厚度的研究

本文给出了用光谱辐射计VF921-256测量大气光学厚度的实验结果，并对其与太阳辐射计DTF-1的同步测量结果进行了比对。实验结果表明：在比较好的大气条件以及对VF921-256测量中所应用的参考板的非朗伯特性进行校正后，两种方法的测量结果符合得较好。因此，在一定的误差范围内，VF921-256可以用于辐射校正中对大气光学厚度的同步测量。最后，本文对上面两种方法各自的测量误差也进行了进一步的分析讨论。     

Combined phase singularity and wavefront analysis for optical maps of ventricular fibrillation

Much of the research into the mechanisms of ventricular fibrillation (VF) employs high-resolution mapping of electrical activation and recovery patterns. We previously developed a method for analyzing electrically mapped VF patterns that was based on identifying individual VF wavefronts. We now introduce a related method designed to take into account the information on repolarization that is present in optically mapped VF data. The new method first converts raw fluorescence data to an angular variable that tracks the phase of the mapped tissue through the depolarization-repolarization cycle. We define wavefronts in this context as isolines of phase that terminate either at boundaries or at singular points within the phase field. These singularities are the pivots of functional reentry and are important determinants of VF patterns. We parameterize VF by constructing data structures that describe wavefronts and singularities and also maintain wavefront-wavefront, wavefront-singularity, and singularity-singularity relationships. We describe one important application of this parameterization, which is to identify, localize, and characterize the importance of occurrences of propagation block during VF.