M Lemay

Spatiotemporal QRST cancellation method using separate QRS and T-waves templates

The standard ECG remains the most common non-invasive tool for diagnosing and studying atrial fibrillation (AF). Due to the much higher amplitude of the electrical ventricular activity in the surface ECG, isolation of the atrial activity component is crucial to the analysis and characterization of AF. An average beat subtraction (ABS) based method is developed to perform this QRST cancellation. In contrast with standard methods, two sets of templates are created instead of one: one set for the QRS complexes and one for the T-waves. The QRS complexes are clustered according to their morphology; the T-waves, using both their preceding RR interval and their morphology. Next, spatial optimization (rotation and scaling) is applied to the QRS templates. ECG signals generated by a biophysical model are used to evaluate the performance of the proposed method in comparison with two other QRST cancellation methods. The proposed method decreases the averaged relative error by 19.7% and 29.0% in comparison with the standard ABS and the standard spatiotemporal method, respectively

Suppression of Ventricular Activity in the Surface Electrocardiogram of Atrial Fibrillation

The analysis of the surface electrocardiogram is potentially useful for the study of atrial fibrillation. Since the ventricular activity is much stronger than the atrial activity, one has to suppress it. To this end, we applied two ICA algorithms to a data set of surface electrocardiogram signals recorded in clinical conditions. We also propose a procedure to judge the quality of the suppression of ventricular activity and the extraction of atrial activity. We apply this procedure to our extracted activities and discuss our results.

Computers in cardiology/physionet challenge 2004: AF classification based on clinical features

The Computers in Cardiology / Physionet Challenge 2004 deals with the classification of ECG signals from AF patients into three categories: types N, S and T corresponding to AF episodes terminating never; soon and immediately, respectively. In our study, diflerent features were used, extracted by the experienced clinician among the authors (LK) on the supplied training set. Algorithms were developped to quantify these features from provided ECG data. A Support Vector Muchine was used to classic these features. In this papel: we present our method, results and conclusion about this clinically-oriented approach.

Estimation of Atrial Multiple Reentrant Circuits from Surface ECG Signals Based on a Vectorcardiographic Approach

This paper presents a simulation study on the identifiability of multiple reentrant circuits on the basis of the vectorcardiogram. The methods involved include an advanced tracking of the basic frequencies of the dominant rotors and a supporting identification based on the observed loops of their vectorcardiogram. The vector cardiogram was derived from body surface potentials spatially sampled by different lead systems. The results indicate that up to three independent circuits can be identified reliably.

Development of a toolbox for electrocardiogram-based interpretation of atrial fibrillation

BACKGROUNDnAtrial fibrillation (AF) develops as a consequence of an underlying heart disease such as fibrosis, inflammation, hyperthyroidism, elevated intra-atrial pressures, and/or atrial dilatation. The arrhythmia is initiated by, or depends on, ectopic focal activity. Autonomic dysfunction may also play a role. However, in most patients, the actual cause of AF is difficult to establish, which hampers the selection of the optimal mode of treatment. This study aims to develop tools for assisting the physicians decision-making process.nnnMETHODSnSignal analytical methods have been developed for optimizing the assessment of the complexity of AF in all of the standard 12-lead signals. The development involved an evaluation of methods for reducing the signal components stemming from the electric activity of the ventricles (QRST suppression). The methods were tested on simulated recordings, on clinical recordings on patients in AF, and on patients exhibiting atrial flutter (AFL) and atrial tachycardia. The results have been published previously. Subsequently, the implementation of the algorithms in a commercially available electrocardiogram (ECG) recorder, an implementation referred to as its AF-Toolbox, has been carried out. The performance of this implementation was tested against those observed during the development stage. In addition, an improved visualization of the specific ECG components was implemented. This was enabled by providing a separate view on ventricular and atrial activity, which resulted from the steps implied in the QRST suppression. Furthermore, a search was initiated for identifying meaningful features in the cleaned up atrial signals.nnnRESULTSnWhen testing the implementation of the previously developed methods in the Toolbox on simulated and clinical data, the suppression of ventricular activity in the ECG produced residuals down to the level of physiologic background noise, in agreement with those reported on previously. The QRST suppression resulted in a better visualization of the atrial signals in AF, atrial AFL, sinus rhythm in the presence of atrioventricular blocks, or ectopic beats. Classifiers for AF and AFL that have been defined so far include the distinct spectral components (multiple basic frequencies), exhibiting distinct dominance in specific leads. The annotations of ventricular and atrial activities, ventricular and atrial trigger, as well as ratio between atrial and ventricular rates were greatly facilitated. The time diagram of ventricular and atrial triggers provides an additional view on rhythm disturbances.nnnCONCLUSIONSnThe AF-Toolbox that is currently developed for clinical applications has the potential of reliably detecting and classifying AF, as well as to correctly describe atrioventricular conduction, propagation blocks and/or ectopic beats. Based on the results obtained, a first industrial prototype has been built, which will be used to assess its performance in a routine clinical environment. The availability of this tool will facilitate the search for meaningful signal features for identifying the source of AF in individual patients.

The Role of Atrial Modeling in the Development of ECG Processing Tools

The standard ECG remains the most common non-invasive tool for assessing atrial fibrillation. Specific signal processing techniques have been developed to improve the diagnosis. However, validation of such tools is challenging and comprehensive invasive data may not easily be obtained. To facilitate this task, we developed a computer model of the atria. In this electrophysiological model, atrial fibrillation was simulated and the manifestation of its electrical activity on the thorax was computed. The resulting realistic-looking synthetic ECG signals were used as benchmarks for testing, evaluating and comparing ECG processing techniques such as cancella-tion of the ventricular activity, vectorcardiography and domi-nant frequency analysis.