Atrial Fibrillation (AF) is the most common tachyarrhythmia in the heart. Irregular RR intervals and the absence of a P wave before the QRS complex characterize AF. Although many studies have been done to detect atrial fibrillation, many aspects of this intricate disease need further analysis. AF is often diagnosed by the interpretation of multi-lead electrocardiograms (ECGs), which provide a non-invasive comprehensive evaluation of cardiac electrical activity. However, due to the poor spatial resolution of ECG recordings, the characteristics of AF cannot be fully demonstrated by using multi-lead ECG solely. Better spatial resolution is obtained by using high-resolution epicardial electrograms measured directly at the surface of the heart. The combination of multi-lead ECGs and high-resolution electrograms should provide a more detailed view of atrial fibrillation. To analyze such data, we first need to derive relevant features that can reduce the data and capture their essence. An initial aim of this research is to increase the accuracy of AF detection and identification of electropathological regions within the heart using the derived features. The ultimate goal is to (non-invasively) monitor the progression of atrial fibrillation through its subsequent stages....@en

Atrial fibrillation (AF) is the most sustained arrhythmia in the heart and also the most common complication developed after cardiac surgery. Due to its progressive nature, timely detection of AF is important. Currently, physicians use a surface electrocardiogram (ECG) for AF diagnosis. However, when the patient develops AF, its various development stages are not distinguishable for cardiologists based on visual inspection of the surface ECG signals. Therefore, severity detection of AF could start from differentiating between short-lasting AF and long-lasting AF. Here, de novo post-operative AF (POAF) is a good model for short-lasting AF while long-lasting AF can be represented by persistent AF. Therefore, we address in this paper a binary severity detection of AF for two specific types of AF. We focus on the differentiation of these two types as de novo POAF is the first time that a patient develops AF. Hence, comparing its development to a more severe stage of AF (e.g., persistent AF) could be beneficial in unveiling the electrical changes in the atrium. To the best of our knowledge, this is the first paper that aims to differentiate these different AF stages. We propose a method that consists of three sets of discriminative features based on fundamentally different aspects of the multi-channel ECG data, namely based on the analysis of RR intervals, a greyscale image representation of the vectorcardiogram, and the frequency domain representation of the ECG. Due to the nature of AF, these features are able to capture both morphological and rhythmic changes in the ECGs. Our classification system consists of a random forest classifier, after a feature selection stage using the ReliefF method. The detection efficiency is tested on 151 patients using 5-fold cross-validation. We achieved 89.07% accuracy in the classification of de novo POAF and persistent AF. The results show that the features are discriminative to reveal the severity of AF. Moreover, inspection of the most important features sheds light on the different characteristics of de novo post-operative and persistent AF.

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Atrial Fibrillation (AF) is the most sustained arrhythmia in the heart. On the surface electrocardiogram (ECG), AF is characterised by the irregular RR intervals and by fibrillatory waves or the absence of a P wave. Since AF is a progressive disease, timely and correct detection is crucial for AF treatment. Detailed insight into the areas of arrhythmia-related electropathology can be obtained by analyzing high-resolution (inter-electrode distance 2mm) electrograms (EGMs). However, these measurements are rather invasive. By integration of high-resolution epicardial mapping data and surface ECG data, we hope to learn how different stages of AF represent themselves on the ECG. Eventually this can help to guide to identify areas of electropathology as target sites of ablation therapy on the less invasive ECG. A first step in this direction is to learn how to reconstruct the ECG based on EGM measurements. In practice, however, EGMs are only measured at few atrial locations, not covering the complete atria. An important question therefore is: How can we reconstruct ECG based on the observations from a limited part of the heart? To answer this question, we propose two methods. In the first method, we increase the number of observations from a part of the right atrium (RA) to the whole RA by synchronizing EGMs that are measured at different moments in time based on the local activation time (LAT). In the second method, under the assumption that atrial EGMs measured at different spatial areas are linearly related, the conductivity matrix is estimated for the whole atrium which enables us to reconstruct the ECGs from the limited observations. The second method brings twofold benefits. First, the conductivity matrix can be used as a novel diagnostic tool to detect AF as well as areas of electropathology. Second, it provides a practical solution to reconstruct epicardial potentials from ECGs, non- This research was funded in part by the Medical Delta Cardiac Arrhythmia Lab (CAL), the Netherlands. invasively. The results show that method one increases the reconstruction accuracy. Furthermore, the conductivity matrix reveals the structural differences between sinus rhythm (SR) and AF episodes which could be the first step to interpret the underlying electropathology of AF @en

Atrial fibrillation (AF) is the most common arrhythmia in the heart. Two main types of AF are defined as paroxysmal and persistent. In this paper, we present a method to discriminate between the characteristics of paroxysmal and persistent using tensor decompositions of a multi-channel electrocardiogram (ECG) signal. For this purpose, ECG signals are segmented by applying a Hilbert transform on the thresholded signal. Dynamic time warping is used to align the separated segments of each channel and then a tensor is constructed with three dimensions as time, heartbeats and channels. A Canonical polyadic decomposition with rank 2 is computed from this tensor and the resulting loading vectors describe the characteristics of paroxysmal and persistent AF in these three dimensions. The time loading vector reveals the pattern of a single P wave or abnormal AF patterns. The heartbeat loading vector shows whether the pattern is present or absent in a specific beat. The results can be used to distinguish between the patterns in paroxysmal AF and persistent AF. @en