Extraction of the atrial activity from the ECG based on independent component analysis with prior knowledge of the source kurtosis signs

In this work it will be shown that a contrast for independent component analysis based on prior knowledge of the source kurtosis signs (ica-sks) is able to extract atrial activity from the electrocardiogram when a constrained updating is introduced. A spectral concentration measure is used, only allowing signal pair updates when spectral concentration augments. This strategy proves to be valid for independent source extraction with priors on the spectral concentration. Moreover, the method is computationally attractive with a very low complexity compared to the recently proposed methods based on spatiotemporal extraction of the atrial fibrillation signal

Analysis of surface atrial signals using spectral methods for time series with missing data

In this work, the analysis of atrial signals recorded during atrial fibrillation was pursued using two spectral estimators designed for series with missing data: the Lomb periodogram (LP) and the Iterative Singular Spectrum Analysis (ISSA). The main aim is to verify if subtraction ofthe ventricular activity might be avoided by performing spectral analysis on those ECG intervals where such activity is absent, (i.e. the T-Q intervals), at least to estimate the dominant atrial Fibrillatory Frequency (FF). Recordings coming from the 2004 Computers in Cardiology Termination Challenge Database were analyzed. Fibrillatory frequencies were then compared with those obtained from the analysis ofthe correspondent atrial signals extracted using a modified Average Beat Substraction (ABS) technique. We observed that the mean absolute difference was 0.42 \ub1 0.66 Hz for LP, (mean\ub1SD), and 0.39 \ub1 0.64 Hz for ISSA. We concluded that estimation of FF is feasible without applying QRS-T subtraction

Wavelet entropy as a measure of ventricular beat suppression from the electrocardiogram in atrial fibrillation

A novel method of quantifying the effectiveness of the suppression of ventricular activity from electrocardiograms (ECGs) in atrial fibrillation is proposed. The temporal distribution of the energy of wavelet coefficients is quantified by wavelet entropy at each ventricular beat. More effective ventricular activity suppression yields increased entropies at scales dominated by the ventricular and atrial components of the ECG. Two studies are undertaken to demonstrate the efficacy of the method: first, using synthesised ECGs with controlled levels of residual ventricular activity, and second, using patient recordings with ventricular activity suppressed by an average beat template subtraction algorithm. In both cases wavelet entropy is shown to be a good measure of the effectiveness of ventricular beat suppression

Source Classification in Atrial Fibrillation Using a Machine Learning Approach

International audienceA precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary for a better understanding of the mechanisms behind atrial fibrillation (AF). Blind source separation (BSS) techniques have proven useful in extracting the AA source from ECG recordings. However, the automated selection of the AA source among the other sources after BSS is still an issue. In this scenario, the present work proposes two contributions: i) the use of the normalized mean square error of the TQ segment (NMSE-TQ) as a new feature to quantify the AA content of a source, and ii) an automated classification of AA and non-AA sources using three well-known machine learning algorithms. The tested classifiers outperform the techniques present in literature. A pattern in the mean and standard deviation of the used features, for AA and non-AA sources, is also observed

Löwner-Based Tensor Decomposition for Blind Source Separation in Atrial Fibrillation ECGs

International audienceThe estimation of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is an important step in the noninvasive analysis of atrial fibrillation (AF), the most common sustained cardiac arrhythmia in clinical practice. Recently, this blind source separation (BSS) problem has been formulated as a tensor factorization, based on the block term decomposition (BTD) of a data tensor built from Hankel matrices of the observed ECG. However, this tensor factorization technique was precisely assessed only in segments with long R-R intervals and with the AA well defined in the TQ segment, where ventricular activity (VA) is absent. Due to the chaotic nature of AA in AF, segments with disorganized or weak AA and with short R-R intervals are quite more common in persistent AF, posing some difficulties to the BSS methods to extract the AA signal, regarding performance and computational cost. In this paper, the BTD built from Löwner matrices is proposed as a method to separate VA from AA in these challenging scenarios. Experimental results obtained in a population of 10 patients show that the Löwner-based BTD outperforms the Hankel-based BTD and two well-known matrix-based methods in terms of atrial signal estimation quality and computational cost

The Relationship between P-Wave Morphology and Atrial Fibrillation

The objective of this paper is to develop an efficient P-wave detection algorithm based on the morphology characteristics of arrhythmias using time domain analysis. ECG from normal subjects, and patients with atrial fibrillation were studied. After baseline wander cancellation, power line interference filtration, the step of QRS detection using the pan- Tompkins algorithm is utilized to calculate R peak which represent the reference point to detect P peak. The algorithm was tested with experiments using MIT-BIH arrhythmia database which included Paroxysmal Atrial Fibrillation PAF prediction challenge, Massachusetts Institute of Technology MIT-BIH normal sinus rhythm, long term Atrial Fibrillation AF and MIT-BIH atrial fibrillation where every P-wave was extracted. The results reveal that the algorithm is accurate and efficient to detect and classify arrhythmias resulted from atrial fibrillation

Lempel-Ziv Complexity Analysis for the Evaluation of Atrial Fibrillation Organization

The Lempel-Ziv (LZ) complexity is a non-linear time series analysis metric that reflects the arising rate of new patterns along with the sequence. Thus, it captures its temporal sequence and, quite conveniently, it can be computed with short data segments. In the present work, a detailed analysis on LZ complexity is presented within the context of atrial fibrillation (AF) organization estimation. As the analysed time series depend on the original sampling rate (fs), we evaluated the relationship between LZ complexity and fs. Furthermore, different implementations of LZ complexity were tested. Our results show the usefulness of LZ complexity to estimate AF organization and suggest that the signals from a terminating paroxysmal AF group are more organized (i.e. less complex) than those from the non-terminating paroxysmal AF group. However, the diagnostic accuracy was not as high as that obtained with sample entropy (SampEn), another non-linear metric, with the same database in a previous study (92% vs. 96%). Nevertheless, the LZ complexity analysis of AF organization with sampling frequencies higher than 2048 Hz, or even its combination with SampEn or other non-linear metrics, might improve the prediction of spontaneous AF termination