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1.
The analysis and characterization of atrial tachyarrhythmias requires, in a previous step, the extraction of the atrial activity (AA) free from ventricular activity and other artefacts. This contribution adopts the blind source separation (BSS) approach to AA estimation from multilead electrocardiograms (ECGs). Previously proposed BSS methods for AA extraction-e.g., independent component analysis (ICA)-exploit only the spatial diversity introduced by the multiple spatially-separated electrodes. However, AA typically shows certain degree of temporal correlation, with a narrowband spectrum featuring a main frequency peak around 3.5-9 Hz. Taking advantage of this observation, we put forward a novel two-step BSS-based technique which exploits both spatial and temporal information contained in the recorded ECG signals. The spatiotemporal BSS algorithm is validated on simulated and real ECGs from a significant number of atrial fibrillation (AF) and atrial flutter (AFL) episodes, and proves consistently superior to a spatial-only ICA method. In simulated ECGs, a new methodology for the synthetic generation of realistic AF episodes is proposed, which includes a judicious comparison between the known AA content and the estimated AA sources. Using this methodology, the ICA technique obtains correlation indexes of 0.751, whereas the proposed approach obtains a correlation of 0.830 and an error in the estimated signal reduced by a factor of 40%. In real ECG recordings, we propose to measure performance by the spectral concentration (SC) around the main frequency peak. The spatiotemporal algorithm outperforms the ICA method, obtaining a SC of 58.8% and 44.7%, respectively.  相似文献   

2.
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.  相似文献   

3.
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.  相似文献   

4.
This contribution addresses the extraction of atrial activity (AA) from real electrocardiogram (ECG) recordings of atrial fibrillation (AF). We show the appropriateness of independent component analysis (ICA) to tackle this biomedical challenge when regarded as a blind source separation (BSS) problem. ICA is a statistical tool able to reconstruct the unobservable independent sources of bioelectric activity which generate, through instantaneous linear mixing, a measurable set of signals. The three key hypothesis that make ICA applicable in the present scenario are discussed and validated: 1) AA and ventricular activity (VA) are generated by sources of independent bioelectric activity; 2) AA and VA present non-Gaussian distributions; and 3) the generation of the surface ECG potentials from the cardioelectric sources can be regarded as a narrow-band linear propagation process. To empirically endorse these claims, an ICA algorithm is applied to recordings from seven patients with persistent AF. We demonstrate that the AA source can be identified using a kurtosis-based reordering of the separated signals followed by spectral analysis of the sub-Gaussian sources. In contrast to traditional methods, the proposed BSS-based approach is able to obtain a unified AA signal by exploiting the atrial information present in every ECG lead, which results in an increased robustness with respect to electrode selection and placement.  相似文献   

5.
Atrial fibrillation (AF) is a common clinical problem, associated with considerable morbidity and motility, for which effective management strategies have yet to be devised. The absence of objective measures to guide selection of antiarrhythmic drug therapy for maintenance of sinus rhythm leaves only clinical endpoints (either beneficial or detrimental) for assessment of drug action, with occasional catastrophic consequences. As part of an attempt to provide an objective framework for the assessment of antiarrhythmic drug action on the electrophysiologic determinants of atrial fibrillation, the authors have developed a measure of the spatial organization of atrial activation processes during atrial fibrillation. By recording activation sequences at multiple equally spaced locations on the endocardial surface of the atrial during atrial fibrillation in humans and determining the degree of correlation between these activation sequences as a function of distance, the authors have been able to construct spatial correlation functions for atrial activation. They have found that atrial activation remains well-correlated, independent of distance during normal sinus rhythm and atrial flutter. During atrial fibrillation, correlation decays monotonically with distance and the space-constant for this decay may be used to describe the relative spatial organization of atrial fibrillation. The authors provide examples of the impact of antiarrhythmic agents on the space-constant and suggest that assessment of the relative spatial organization of atrial activation using this methodology may potentially provide an objective framework to guide therapy in patients with AF  相似文献   

6.
Phase-rectified signal averaging (PRSA) is a technique recently introduced to enhance quasi-periodic signal components. An important parameter that can be extracted from surface ECG is the dominant frequency (DF) of atrial fibrillation (AF). AF signal components are always highly contaminated by the ventricular complexes, and the cancellation of these components is never perfect. The remaining artifacts tend to induce erroneous DF estimates. In this paper, we report on the use of PRSA in the context of noninvasive AF classification procedures for improving DF estimation. The potential of PRSA is demonstrated by experiments both on synthetic and clinical ECG signals.  相似文献   

7.
A novel method for characterization of f-wave morphology in atrial fibrillation (AF) is presented. The method decomposes atrial activity into fundamental and harmonic components, dividing each component into short blocks for which the amplitudes, frequencies, and phases are estimated. The phase delays between the fundamental and each of the harmonics, here referred to as harmonic phase relationships, are used as features of f-wave morphology. The estimated waves are clustered into typical morphologic patterns. The performance of the method is illustrated by simulated signals, ECG signals recorded from 36 patients with organized AF, and an ECG signal recorded during drug loading with flecainide. The results show that the method can distinguish a wide variety of f-wave morphologies, and that typical morphologies can be established for further analysis of AF.   相似文献   

8.
Due to the much higher amplitude of the electrical activity of the ventricles in the surface electrocardiogram (ECG), its cancellation is crucial for the analysis and characterization of atrial fibrillation. In this paper, two different methods are proposed for this cancellation. The first one is an average beat subtraction type of method. Two sets of templates are created: one set for the ventricular depolarization waves and one for the ventricular repolarization waves. Next, spatial optimization (rotation and amplitude scaling) is applied to the QRS templates. The second method is a single beat method that cancels the ventricular involvement in each cardiac cycle in an independent manner. The estimation and cancellation of the ventricular repolarization is based on the concept of dominant T and U waves. Subsequently, the atrial activities during the ventricular depolarization intervals are estimated by a weighted sum of sinusoids observed in the cleaned up segments. ECG signals generated by a biophysical model as well as clinical ECG signals are used to evaluate the performance of the proposed methods in comparison to two standard ABS-based methods.  相似文献   

9.
Atrial fibrillation (AF) is the most common cardiac arrhythmia and entails an increased risk of thromboembolic events. Prediction of the termination of an AF episode, based on noninvasive techniques, can benefit patients, doctors and health systems. The method described in this paper is based on two-lead surface electrocardiograms (ECGs): 1-min ECG recordings of AF episodes including N-type (not terminating within an hour after the end of the record), S-type (terminating 1 min after the end of the record) and T-type (terminating immediately after the end of the record). These records are organised into three learning sets (N, S and T) and two test sets (A and B). Starting from these ECGs, the atrial and ventricular activities were separated using beat classification and class averaged beat subtraction, followed by the evaluation of seven parameters representing atrial or ventricular activity. Stepwise discriminant analysis selected the set including dominant atrial frequency (DAF, index of atrial activity) and average HR (HRmean, index of ventricular activity) as optimal for discrimination between N/T-type episodes. The linear classifier, estimated on the 20 cases of the N and T learning sets, provided a performance of 90% on the 30 cases of a test set for the N/T-type discrimination. The same classifier led to correct classification in 89% of the 46 cases for N/S-type discrimination. The method has shown good results and seems to be suitable for clinical application, although a larger dataset would be very useful for improvement and validation of the algorithms and the development of an earlier predictor of paroxysmal AF spontaneous termination time.  相似文献   

10.
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.  相似文献   

11.
Automatic detection of atrial fibrillation (AF) for AF diagnosis, especially for AF monitoring, is necessarily desirable for clinical therapy. In this study, we proposed a novel method for detection of the transition between AF and sinus rhythm based on RR intervals. First, we obtained the delta RR interval distribution difference curve from the density histogram of delta RR intervals, and then detected its peaks, which represented the AF events. Once an AF event was detected, four successive steps were used to classify its type, and thus, determine the boundary of AF: 1) histogram analysis; 2) standard deviation analysis; 3) numbering aberrant rhythms recognition; and 4) Kolmogorov-Smirnov (K-S) test. A dataset of 24-h Holter ECG recordings (n = 433) and two MIT-BIH databases (MIT-BIH AF database and MIT-BIH normal sinus rhythm (NSR) database) were used for development and evaluation. Using the receiver operating characteristic curves for determining the threshold of the K-S test, we have achieved the highest performance of sensitivity and specificity (SP) (96.1% and 98.1%, respectively) for the MIT-BIH AF database, compared with other previously published algorithms. The SP was 97.9% for the MIT-BIH NSR database.  相似文献   

12.
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  相似文献   

13.
This study tests the hypothesis that atrial fibrillation (AFib) can be discriminated from regular atrial rhythms by a measure of the variation in local activation direction. Human endocardial atrial recordings of AFib, sinus rhythm, atrial flutter, and supraventricular tachycardia were collected using a catheter with orthogonally placed electrodes, and the direction of each activation was calculated using methods previously described by our laboratory. Each recording was divided into segments containing 100 activations, and the spatial precision for each segment was calculated in three dimensions, as well as in each of the three two-dimensional (2-D) planes. The three-dimensional (3-D) spatial precision for 1161 segments of AFib in 11 recordings ranged from 0.09-0.85 (mean=0.45), whereas the spatial precision for 138 segments of regular rhythms in 28 recordings was ⩾0.91 in all but four instances. The 2-D spatial precision values overlapped for all rhythms. The results indicate that 3-D spatial precision of local activation direction is a useful discriminator of AFib  相似文献   

14.
Detecting ventricular tachycardia and fibrillation by complexity measure   总被引:9,自引:0,他引:9  
Sinus rhythm (SR), ventricular tachycardia (VT) and ventricular fibrillation (VF) belong to different nonlinear physiological processes with different complexity. In this study, we present a novel, and computationally fast method to detect VT and VF, which utilizes a complexity measure suggested by Lempel and Ziv [1]. For a specific window length (i.e., the length of data segment to be analyzed), the method first generates a 0-1 string by comparing the raw electrocardiogram (ECG) data to a selected suitable threshold. The complexity measure can be obtained from the 0-1 string only using two simple operations, comparison and accumulation. When the window length is 7 s, the detection accuracy for each of SR, VT, and VF is 100% for a test set of 204 body surface records (34 SR, 85 monomorphic VT, and 85 VF). Compared with other conventional time- and frequency-domain methods, such as rate and irregularity, VF-filter leakage, and sequential hypothesis testing, the new algorithm is simple, computationally efficient, and well suited for real-time implementation in automatic external defibrillators (AED's).  相似文献   

15.
Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from the 12-lead ECG. The 12-lead ECGs of 30 patients in atrial fibrillation were analyzed. Atrial activity was extracted by three algorithms, Spatiotemporal QRST cancellation (STC), principal component analysis (PCA), and independent component analysis (ICA). The amplitude and frequency characteristics of the extracted atrial signals were compared between algorithms and against reference data. Mean (standard deviation) amplitude of QRST segments of V1 was 0.99 (0.54) mV, compared to 0.18 (0.11) mV (STC), 0.19 (0.13) mV (PCA), and 0.29 (0.22) mV (ICA). Hence, for all algorithms there were significant reductions in the amplitude of the ventricular activity compared with that in V1. Reference atrial signal amplitude in V1 was 0.18 (0.11) mV, compared to 0.17 (0.10) mV (STC), 0.12 (0.09) mV (PCA), and 0.18 (0.13) mV (ICA) in the extracted atrial signals. PCA tended to attenuate the atrial signal in these segments. There were no significant differences for any of the algorithms when comparing the amplitude of the reference atrial signal with that of the extracted atrial signals in segments in which ventricular activity had been removed. There were no significant differences between algorithms in the frequency characteristics of the extracted atrial signals. There were discrepancies in amplitude and frequency characteristics of the atrial signal in only a few cases resulting from notable residual ventricular activity for PCA and ICA algorithms. In conclusion, the extracted atrial signals from these algorithms exhibit very similar amplitude and frequency characteristics. Users of these algorithms should be observant of residual ventricular activities which can affect the analysis of the fibrillatory waveform in clinical practice.  相似文献   

16.
This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a feature-extraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic system was applied on 100 intracardiac AF signal strips and on a selection of 11 representative features, demonstrating: a) the possibility to properly identify the most significant features for the discrimination of AF types; b) higher accuracy (97.7% using the seven most informative features) than the traditional maximum likelihood classifier; and c) effectiveness in AF classification also with few training samples (accuracy = 88.3% with only five training signals). Finally, the system identifies a combination of indices characterizing changes of morphology of atrial activation waves and perturbation of the isoelectric line as the most effective in separating the AF types.  相似文献   

17.
Atrial fibrillation (AF) has been described as a "random" or "chaotic" rhythm. Evidence suggests that AF may have transient episodes of temporal and spatial organization. We introduce a new algorithm that quantifies AF organization by the mean-squared error (MSE) in the linear prediction between two cardiac electrograms. This algorithm calculates organization at a finer temporal resolution. (approximately 300 ms) than previously published algorithms. Using canine atrial epicardial mapping data, we verified that the MSE algorithm showed nonfibrillatory rhythms to be significantly more organized than fibrillatory rhythms (p < .00001). Further, we compared the sensitivity of MSE to that of two previously published algorithms by analyzing AF with simulated noise and AF manipulated with vagal stimulation or by adenosine administration to alter the character of the AF. MSE performed favorably in the presence of noise. While all three algorithms distinguished between low and high vagal AF, MSE was the most sensitive in its discrimination. Only MSE could distinguish baseline AF from AF with adenosine. We conclude that our algorithm can distinguish different levels of organization during AF with a greater temporal resolution and sensitivity than previously described algorithms. This algorithm could lead to new ways of analyzing and understanding AF as well as improved techniques in AF therapy.  相似文献   

18.
The inability to detect atrial activity limits implantable ventricular cardioverter defibrillators (ICD) in discriminating tachycardias and can result in inappropriate therapy. This study attempted to detect atrial activity on the wide-spaced bipole signals formed by the high-voltage (HV) leads of the ICD during device implantation and to develop an algorithm for the detection of atrial fibrillation (AFib) from these signals. The authors used a method that cancelled ventricular and correlated atrial activity from the HV lead signals and measured frequency and amplitude distribution information to discriminate sinus rhythm (SR) and AFib segments. The authors analyzed 186 data segments from 21 patients (six AFib, 14 SR, one AFib and SR). For individual segments in this data set, the sensitivity of the algorithm was 78%, specificity 92.65%, positive and negative predictive values 79.59 and 91.97%, respectively. These results demonstrate that atrial activity is present in the HV lead signals, and AFib detection can be achieved in many, but not all cases, using information currently available to ICDs. Prior work from surface electrocardiograms suggests that this algorithm can function during ventricular tachycardias. However, specificity of the algorithm is not high enough for clinical use  相似文献   

19.
To further clarify the mechanisms maintaining chronic atrial fibrillation (CAF), a method identifying preferable activation patterns of the atria during fibrillation, by time averaging of multiple discrete excitation vectors, was developed. Repeated recordings, each of 56 atrial bipolar electrograms simultaneously acquired during 8 s, were made at multiple sites in the right atrial free wall and the left atrial appendage in 16 patients with CAF using a 2.17×3.54 cm electrode array. The local activation times (LAT's) in each recording were estimated as the median activation time at the respective measurement point. By calculating the time difference between the LAT's at adjacent measurement points in two spatial dimensions, a direction vector was created for each activation wave passing each set of measurement points, a total of 42 sets. By time averaging of the individual direction vectors (typically n=55) at each set of measurement points, preferable activation patterns were determined. Three types of activation patterns were found: 1) inconsistent activation (n=5), 2) consistent activation with preferential propagation directions (n=7) and 3) consistent activation with impulses originating from a localizable site within the recording area (n=4). All activation patterns were reproducible and the two latter patterns were proven significant using statistical tests. It is concluded that this new method is useful in further clarification of the mechanisms involved in the maintenance of atrial fibrillation  相似文献   

20.
Recent studies have suggested that the initial therapeutic intervention for ventricular fibrillation (VF) may depend on downtime (DT), i.e., the time duration of VF. We characterized the dynamics of the frequency distribution in the power spectrum of the ECG recorded from eleven swine during VF to determine if enough information existed in this domain to estimate DT. We used the median frequency (FM) of the power spectrum to track the frequency distribution. The FM followed a dynamic repeatable course during the first 10 min of VF. Intersubject variability was small. We modeled the FM data of the eleven subjects with a set of first-order polynomial equations and tested the algorithm with data from an additional ten subjects. The algorithm predicted VF duration with an average error of -0.86 min; 71.5% of the predictions fell within the 95% confidence limits of the model. This paper has identified a signal processing tool which may be useful in the prehospital treatment of VF.  相似文献   

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