P-wave morphology assessment by a gaussian functions-based model in atrial fibrillation patients

Aim of this study was to present a P-wave model, based on a linear combination of Gaussian functions, to quantify morphological aspects of P-wave in patients prone to atrial fibrillation (AF). Five-minute ECG recordings were performed in 25 patients with permanent dual chamber pacemakers. Patients were divided into high-risk and low-risk groups, including patients with and without AF episodes in the last 6 mo preceding the study, respectively. ECG signals were acquired using a 32-lead mapping system for high-resolution biopotential measurement (ActiveTwo, Biosemi, The Netherlands, sample frequency 2 kHz, 24-bit resolution). Up to 8 Gaussian models have been computed for each averaged P-wave extracted from every lead. The P-wave morphology was evaluated by extracting seven parameters. Classical time-domain parameters, based on P-wave duration estimation, have been also estimated. We found that the P-wave morphology can be effectively modeled by a linear combination of Gaussian functions. In addition, the combination of time-domain and morphological parameters extracted from the Gaussian function-based model of the P-wave improves the identification of patients having different risks of developing AF.     

Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias

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.     

Phase-rectified signal averaging used to estimate the dominant frequencies in ECG signals during atrial fibrillation

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.     

Analyzing high-density ECG signals using ICA

The analysis of ECG signals is of fundamental importance for cardiac diagnosis. Conventional ECG recordings, however, use a limited number of channels (12) and each records a mixture of activities generated in different parts of the heart. Therefore, direct observation of the ECG signals collected on the body surface is likely an inefficient way to study and diagnose cardiac abnormalities. This study describes new experimental and analytical methods to capture more meaningful ECG component signals, each representing more directly a physical cardiac source. This study first describes a simply applied method for collecting high-density ECG signals. The recorded signals are then separated by independent component analysis (ICA) to obtain spatially fixed and temporally independent component activations. Results from five subjects show that P-, QRS-, and T-waves can be clearly separated from the recordings, suggesting ICA might be an effective and useful tool for high-density ECG analysis, interpretation, and diagnosis.      

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.     

Cancellation of ventricular activity in the ECG: evaluation of novel and existing methods

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.     

How Many Leads Are Necessary for a Reliable Reconstruction of Surface Potentials During Atrial Fibrillation?

In this study, we aimed at determining how many leads are necessary for accurately reconstructing ECG potentials during atrial fibrillation (AF) on the body surface. Although the standard ECG is appropriate for the detection of this arrhythmia, its accuracy for extracting other diagnostic features or constructing surface potential maps may not be optimal. We evaluated the suitability of the standard ECG in AF and proposed a new lead system for improving the information content of AF signals in limited lead systems. We made use of 64-lead body surface potential mapping recordings of 17 patients during AF and 18 healthy subjects. Lead selection was performed by making use of a lead selection algorithm proposed by Lux, and error curves were calculated for increasing number of selected leads for QRS complexes and P waves from healthy subjects and AF signals. From our results, at least 23 leads are needed in order to have the same degree of accuracy in the derivation of AF waves as the 12-lead ECG for a normal QRS complex (25% error). The 12-lead ECG allows a reconstruction of surface potentials with 53% error. If a limited lead set is to be chosen, a repositioning of only four electrodes from the standard ECG reduces reconstruction error in 11%. This repositioning of electrodes may include more right anterior electrodes and one posterior electrode.     

Atrial activity extraction for atrial fibrillation analysis using blind source separation

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.     

Temporal and spatial phase analyses of the electrocardiogram stratify intra-atrial and intra-ventricular organization

We hypothesized that electrocardiogram (ECG) spatial phase analysis would define a spectrum of intracardiac organization from atrial fibrillation (AF), nonisthmus-dependent and isthmus-dependent atrial flutter (AFL) to supraventricular tachycardias (SVT), and similarly for ventricular arrhythmias. We analyzed arrhythmia ECGs of 33 patients with isthmus (n = 9) and nonisthmus (n = 5) dependent AFL and SVT: atrial (n = 3), atrioventricular nodal (n = 3), and orthodromic reciprocating (n = 3) tachycardias, as well as AF (n = 5), ventricular tachycardia (monomorphic, VT-MM; n = 7), and fibrillation (VF; n = 3). ECG spatial phase was considered coherent when the correlation coefficient of an atrial (or ventricular) template to its ECG over time maintained a constant relationship in XY, XZ, and YZ planes. Regularity was quantified spectrally from ECG and correlation series. Spatial coherence occurred in 9/9 cases of isthmus--but only 1/5 of cases of nonisthmus-dependent AFL (p < 0.01; chi2). All showed one dominant spectral peak (temporal coherence). In AF, spatial phase was inconsistent in all planes and spectra were broad band. Temporal and spatial coherence occurred in other SVT. VT-MM maintained spatial phase and a single spectral peak, while VF displayed neither. Our conclusions are that temporal and spatial phase analysis from the ECG stratifies intra-atrial and intra-ventricular organization and reveals subtle variability lost on visual inspection.     

Frequency tracking of atrial fibrillation using hidden Markov models.

A hidden Markov model (HMM) is employed to improve noise robustness when tracking the dominant frequency of atrial fibrillation (AF) in the electrocardiogram (ECG). Following QRST cancellation, a sequence of observed frequency states is obtained from the residual ECG, using the short-time Fourier transform. Based on the observed state sequence, the Viterbi algorithm retrieves the optimal state sequence by exploiting the state transition matrix, incorporating knowledge on AF characteristics, and the observation matrix, incorporating knowledge of the frequency estimation method and signal-to-noise ratio (SNR). The tracking method is evaluated with simulated AF signals to which noise, obtained from ECG recordings, has been added at different SNRs. The results show that the use of HMM improves performance considerably by reducing the rms error associated with frequency tracking: at 4-dB SNR, the rms error drops from 0.2 to 0.04 Hz.     

Noninvasive ECG as a tool for predicting termination of paroxysmal atrial fibrillation

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.     

Homomorphic Analysis and Modeling of ECG Signals

Homomorphic analysis and pole-zero modeling of electrocardiogram (ECG) signals are presented in this paper. Four typical ECG signals are considered and deconvolved into their minimum and maximum phase components through cepstral filtering, with a view to study the possibility of more efficient feature selection from the component signals for diagnostic purposes. The complex cepstra of the signals are linearly filtered to extract the basic wavelet and the excitation function. The ECG signals are, in general, mixed phase and hence, exponential weighting is done to aid deconvolution of the signals. The basic wavelet for normal ECG approximates the action potential of the muscle fiber of the heart and the excitation function corresponds to the excitation pattern of the heart muscles during a cardiac cycle. The ECG signals and their components are pole-zero modeled and the pole-zero pattern of the models can give a clue to classify the normal and abnormal signals. Besides, storing only the parameters of the model can result in a data reduction of more than 3:1 for normal signals sampled at a moderate 128 samples/s.     

Convolutive blind source separation algorithms applied to the electrocardiogram of atrial fibrillation: study of performance

The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of atrial fibrillation (AF). In order to use the ECG as a tool for the analysis of AF, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, statistical signal processing techniques, like blind source separation (BSS), are able to perform a multilead statistical analysis with the aim to obtain the AA. Linear BSS techniques can be divided in two groups depending on the mixing model: algorithms where instantaneous mixing of sources is assumed, and convolutive BSS (CBSS) algorithms. In this work, a comparison of performance between one relevant CBSS algorithm, namely Infomax, and one of the most effective independent component analysis (ICA) algorithms, namely FastICA, is developed. To carry out the study, pseudoreal AF ECGs have been synthesized by adding fibrillation activity to normal sinus rhythm. The algorithm performances are expressed by two indexes: the signal to interference ratio (SIRAA) and the cross-correlation (RAA) between the original and the estimated AA. Results empirically prove that the instantaneous mixing model is the one that obtains the best results in the AA extraction, given that the mean SIRAA obtained by the FastICA algorithm (37.6 +/- 17.0 dB) is higher than the main SIRAA obtained by Infomax (28.5 +/- 14.2 dB). Also the RAA obtained by FastICA (0.92 +/- 0.13) is higher than the one obtained by Infomax (0.78 +/- 0.16).     

Two Methods for Optimal MECG Elimination and FECG Detection from Skin Electrode Signals

Two signal processing techniques for the suppression of the maternal ECG and simultaneously optimal detection of fetal ECG with respect to noise are presented. Both techniques are based on the singular value decomposition of a measurement matrix. Criteria are given in order to evaluate, a priori, electrode locations and sampling schemes for both methods. A fundamental difference with other methods is that the number of linearly independent FECG signals is not constrained to one. One of the presented techniques is a typical offline method. It is well suited for a large number of electrodes and large number of samples, which results in a better signal to noise ratio. The second technique is a typical on-line method. It gives fetal ECG signals within about 1 s, and is adaptive to changes of the transfer (e.g., due to fetal movement). It can be applied with a small number of electrodes (e.g., eight). It is shown that if three of these signals are from thoracic electrodes, the MECG suppression is guaranteed.     

Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation

A new method for QRST cancellation is presented for the analysis of atrial fibrillation in the surface electrocardiogram (ECG). The method is based on a spatiotemporal signal model which accounts for dynamic changes in QRS morphology caused, e.g., by variations in the electrical axis of the heart. Using simulated atrial fibrillation signals added to normal ECGs, the results show that the spatiotemporal method performs considerably better than does straightforward average beat subtraction (ABS). In comparison to the ABS method, the average QRST-related error was reduced to 58 percent. The results obtained from ECGs with atrial fibrillation agreed very well with those from simulated fibrillation signals.     

Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy

We hypothesized that frequency domain analysis of an interatrial atrial fibrillation (AF) electrogram would show a correlation of the variance of the signal and the amplitude of harmonic peaks with the periodicity and morphology (organization) of the AF signal and defibrillation efficacy. We sought to develop an algorithm that would provide a high-resolution measurement of the changes in the spatiotemporal organization of AF. AF was initiated with burst atrial pacing in ten dogs. The atrial defibrillation threshold (ADFT50) was determined, and defibrillation was repeated at the ADFT50. Bipolar electrograms from the shocking electrodes were acquired immediately preshock, digitally filtered, and a FFT was performed. The organization index (OI) was calculated as the ratio of the area under the first four harmonic peaks to the total area of the spectrum. For a 4-s window, the mean OI was 0.505 +/- 0.087 for successful shocks, versus 0.352 +/- 0.068 for unsuccessful shocks (p < 0.001). Receiver operator characteristic (ROC) curve analysis was used to determine the optimal sampling window for predicting successful shocks. The area of the ROC curve was 0.8 for a 1-s window, and improved to 0.9 for a 4-s window. We conclude that the spectrum of an AF signal contains information relating to its organization, and can be used in predicting a successful defibrillation.     

Extraction and analysis of T waves in electrocardiograms during atrial flutter

Analysis of T waves in the ECG is an essential clinical tool for diagnosis, monitoring, and follow-up of patients with heart dysfunction. During atrial flutter, this analysis has been so far limited by the perturbation of flutter waves superimposed over the T wave. This paper presents a method based on missing data interpolation for eliminating flutter waves from the ECG during atrial flutter. To cope with the correlation between atrial and ventricular electrical activations, the CLEAN deconvolution algorithm was applied to reconstruct the spectrum of the atrial component of the ECG from signal segments corresponding to TQ intervals. The locations of these TQ intervals, where the atrial contribution is presumably dominant, were identified iteratively. The algorithm yields the extracted atrial and ventricular contributions to the ECG. Standard T-wave morphology parameters (T-wave amplitude, T peak-T end duration, QT interval) were measured. This technique was validated using synthetic signals, compared to average beat subtraction in a patient with a pacemaker, and tested on pseudo-orthogonal ECGs from patients in atrial flutter. Results demonstrated improvements in accuracy and robustness of T-wave analysis as compared to current clinical practice.     

基于多点平均频率校正的多表面波长调谐移相干涉测量

为实现多表面干涉测量中强度叠加干涉信号的分离和相位解调,提出了一种基于频率校正的多表面波长移相干涉测量算法,可实现透明被测件各表面面形的同时重建。波长移相干涉技术可以根据各干涉谐波光程差(optical path difference, OPD)的不同使各表面干涉谐波具有不同的移相值,该差异为各信号分离和相位解调提供了基础。在现有的多表面测量技术中,往往通过被测件的腔长和光学厚度等信息对谐波频率进行粗估,但估计精度较低,且无法应对移相误差。因此,本文通过多点平均和频率校正实现了各干涉谐波频率的精确提取,能够有效消除异常值和加性高斯噪声(additive Gaussian noise, AGN)对频率求解精度的影响,并且仅通过干涉图之间的加权操作便可同时对各谐波相位进行解调,对比分析和实验结果验证了所提出的算法的可靠性。     

A robust method for ECG-based estimation of the respiratory frequency during stress testing

A robust method is presented for electrocardiogram (ECG)-based estimation of the respiratory frequency during stress testing. Such ECGs contain highly nonstationary noise and exhibit changes in QRS morphology which, when combined with the dynamic nature of the respiratory frequency, make most existing methods break down. The present method exploits the oscillatory pattern of the rotation angles of the heart's electrical axis as induced by respiration. The series of rotation angles, obtained from least-squares loop alignment, is subject to power spectral analysis and estimation of the respiratory frequency. Robust techniques are introduced to handle the nonstationary properties of exercise ECGs. The method is evaluated by means of both simulated signals, and ECG/airflow signals recorded from 14 volunteers and 20 patients during stress testing. The resulting respiratory frequency estimation error is, for simulated signals, equal to 0.5% +/- 0.2%, mean +/- SD (0.002 +/- 0.001 Hz), whereas the error between respiratory frequencies of the ECG-derived method and the airflow signals is 5.9% +/- 4% (0.022 +/- 0.016Hz). The results suggest that the method is highly suitable for analysis of noisy ECG signals recorded during stress testing.     

An Orthogonal Design of Experiments for Accurately Estimating Harmonics of Periodic Signals at Low Frequencies

The lack of synchronization between the sampling rate and the signal frequency is the main source of leakage errors in the harmonic analysis of periodic signals performed by means of digital techniques. An algorithm for accurately measuring the harmonic parameters of low-frequency, arbitrary voltage signals without using synchronization circuits was published recently. It is shown that the algorithm is an alternative orthogonal design of experiments for the problem of fitting a linear trigonometric model to integrating digital voltmeter data. This was experimentally confirmed in recent comparison of the proposed method with a synchronous synthesizing and sampling system. The harmonic magnitudes as a percentage of the fundamental measured by both methods differ by less than one part in 10^{6}. The algorithm can be advantageously used in almost any kind of low-frequency ac applications where two arbitrary voltage signals measured by two voltmeters are to be compared for harmonic magnitude and phase shift.