(1) Background: Structural remodeling plays an important role in the pathophysiology of atrial fibrillation (AF). It is likely that structural remodeling occurs transmurally, giving rise to electrical endo-epicardial asynchrony (EEA). Recent studies have suggested that areas of EEA may be suitable targets for ablation therapy of AF. We hypothesized that the degree of EEA is more pronounced in areas of transmural conduction block (T-CB) than single-sided CB (SS-CB). This study examined the degree to which SS-CB and T-CB enhance EEA and which specific unipolar potential morphology parameters are predictive for SS-CB or T-CB. (2) Methods: Simultaneous endo-epicardial mapping in the human right atrium was performed in 86 patients. Potential morphology parameters included unipolar potential voltages, low-voltage areas, potential complexity (long double and fractionated potentials: LDPs and FPs), and the duration of fractionation. (3) Results: EEA was mostly affected by the presence of T-CB areas. Lower potential voltages and more LDPs and FPs were observed in T-CB areas compared to SS-CB areas. (4) Conclusion: Areas of T-CB could be most accurately predicted by combining epicardial unipolar potential morphology parameters, including voltages, fractionation, and fractionation duration (AUC = 0.91). If transmural areas of CB indeed play a pivotal role in the pathophysiology of AF, they could theoretically be used as target sites for ablation.

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Background: Ablation strategies targeting fractionated or low-voltage potentials have been widely used in patients with persistent types of atrial fibrillation (AF). However, recent studies have questioned their role in effectively representing sites of conduction slowing, and thus arrhythmogenic substrates. Objectives: The authors studied the relationship between local conduction velocity (CV) and the occurrence of fractionated and/or low-voltage potentials in order to identify areas with critically slowing of conduction. Methods: Intraoperative epicardial mapping was performed during sinus rhythm. Unipolar potentials with an amplitude <1.0 mV were initially classified as low-voltage and potentials with ≥3 deflections as fractionation. A range of thresholds were also explored. Local CV was computed using discrete velocity vectors. Results: A total of 319 patients were included. Fractionated, low-voltage potentials were rare, accounting for only 0.36% (Q1-Q3: 0.15%-0.78%) of all atrial sites. Local CV at sites with fractionated, low-voltage potentials (46.0 cm/s [Q1-Q3: 22.6-72.7 cm/s]) was lowest compared with sites with either low-voltage, nonfractionated potentials (64.5 cm/s [Q1-Q3: 34.8-99.4 cm/s]) or fractionated, high-voltage potentials (65.9 cm/s [Q1-Q3: 41.7-92.8 cm/s]; P < 0.001). Slow conduction areas (CV <50 cm/s) could be most accurately identified by using a low voltage threshold (<1 mV) and a minimum of 3 deflections (positive predictive value: 54.2%-70.7%), although the overall sensitivity remained low (0.1%-1.9%). Conclusions: Sites with fractionated, low-voltage potentials have substantially slower local CV compared with sites with either low-voltage, nonfractionated potentials or fractionated, high-voltage potentials. However, the strong inverse relationship between the positive predictive value and sensitivity of a combined voltage and fractionation threshold for slowed conduction is likely to complicate the use of these signal-based ablation approaches in AF patients.

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The electrical arrhythmogenic substrate underlying the most common cardiac arrhythmia atrial fibrillation (AF) may consist of conduction disorders, low-voltage areas, or fractionated potentials. High-density and resolution epicardial mapping (HDREM) approaches have been introduced to quantify and visualize electrophysiological properties of the atria. These approaches are essential for obtaining innovative insights into arrhythmogenic substrates and identifying novel targets for therapy. The aim of this review is to summarize and discuss the (1) contribution of HDREM studies to the knowledge on atrial arrhythmogenesis and (2) future applications of HDREM of atria in daily clinical practice.

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Objective: The severity of atrial fibrillation (AF) can be assessed from intra-operative epicardial measurements (high-resolution electrograms), using metrics such as conduction block (CB) and continuous conduction delay and block (cCDCB). These features capture differences in conduction velocity and wavefront propagation, but ignore complementary properties such as the morphology of the action potentials. In this work, we focus on such complementary properties, and derive features to detect variations in the atrial potential waveforms. Methods: We show that the spatial variation of atrial potential morphology during a single beat may be described by changes in the singular values of the epicardial measurement matrix. The method is non-parametric and requires little preprocessing. A corresponding singular value map points at areas subject to fractionation and block. Further, we developed an experiment where we simultaneously measure electrograms (EGMs) and a multi-lead ECG. Results: The captured data showed that the normalized singular values of the heartbeats during AF are higher than during SR, and that this difference is more pronounced for the (non-invasive) ECG data than for the EGM data, if the electrodes are positioned at favorable locations. Conclusion: Overall, the singular value-based features are a useful indicator to detect and evaluate AF. Significance: The proposed method might be beneficial for identifying electropathological regions in the tissue without estimating the local activation time.

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Normothermic ex-situ heart perfusion (ESHP) enables assessment of hearts donated after circulatory death (DCD) prior to transplantation. However, sensitive parameters of cardiac function of DCD hearts on ESHP are needed. This study proposes a novel approach using electrophysiological (EP) parameters derived from electrical mapping as biomarkers of post-ischemic cardiac performance. Porcine slaughterhouse hearts (PSH) were divided in two groups based on the type of warm ischemia (Group 1: 10 ± 1 min with animal depilation vs. Group 2: ≤5 min without depilation). Electrical mapping of the right (RV) and left ventricle (LV) was performed on ESHP. Potential voltages, slopes and conduction velocities were computed from unipolar electrograms and compared between groups. Voltages were lower in Group 1 compared to Group 2 (RV: 3.6 vs. 15.3 mV, p = 0.057; LV: 10.8 vs. 23.6 mV, p = 0.029). In addition, the percentage of low-voltage potentials was higher and potential slopes were flatter in Group 1. Voltages and slopes strongly correlated with the visual contractile performance of PSH, but showed weaker correlation with lactate profiles. In conclusion, unipolar potential voltages and potential slopes were decreased in hearts with severe warm ischemia. As such, EP parameters could aid transplantation teams in decision-making on transplantability of DCD hearts.

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Background: Quantified features of local conduction heterogeneity due to pathological alterations of myocardial tissue could serve as a marker for the degree of electrical remodeling and hence be used to determine the stage of atrial fibrillation (AF). Objectives: In this study, the authors investigated whether local directional heterogeneity (LDH) and anisotropy ratio, derived from estimated local conduction velocities (CVs) during AF, are suitable electrical parameters to stage AF. Methods: Epicardial mapping (244-electrode array, interelectrode distance 2.25 mm) of the right atrium was performed during acute atrial fibrillation (AAF) (n = 25, 32 ± 11 years of age) and during long-standing persistent atrial fibrillation (LSPAF) (n = 23, 64 ± 9 years of age). Episodes of 9 ± 4 seconds of AF were analyzed. Local CV vectors were constructed to assess the degree of anisotropy. Directions and magnitudes of individual vectors were compared with surrounding vectors to identify LDH. Results: Compared with the entire AAF group, LSPAF was characterized by slower conduction (71.5 ± 6.8 cm/s vs 67.6 ± 5.6 cm/s; P = 0.037) with a larger dispersion (1.59 ± 0.21 vs 1.95 ± 0.17; P < 0.001) and temporal variability (32.0 ± 4.7 cm/s vs 38.5 ± 3.3 cm/s; P < 0.001). Also, LSPAF was characterized by more LDH (19.6% ± 4.4% vs 26.0% ± 3.4%; P < 0.001) and a higher degree of anisotropy (1.38 ± 0.07 vs 1.51 ± 0.14; P < 0.001). Compared with the most complex type of AAF (type III), LSPAF was still associated with a larger CV dispersion, higher temporal variability of CV, and larger amount of LDH. Conclusions: Increasing AF complexity was associated with increased spatiotemporal variability of local CV vectors, local conduction heterogeneity, and anisotropy ratio. By using these novel parameters, LSPAF could potentially be discriminated from the most complex type of AAF. These observations may indicate pathological alterations of myocardial tissue underlying progression of AF.

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(1) Background. Obesity is a well-established worldwide recognised risk factor for atrial fibrillation (AF). Prior review papers reported on the associations between obesity and AF development, but not on the relation between obesity and atrial electrophysiology. We therefore conducted a systematic review to describe the current knowledge of the characteristics of the atrial electrophysiological substrate in obese individuals and how they relate to the development of AF. (2) Methods. A search was conducted in Pubmed, Embase, and the Cochrane Library for publications evaluating the impact of obesity on atrial electrophysiology, electrical substrates, and their relation to the development of AF. (3) Results. A systematic literature search retrieved 477 potential publications based on the inclusion criteria; 76 full-text articles were selected for the present systematic review. The literature demonstrated that obesity predisposes to not only a higher AF incidence but also to more extensive atrial electrophysiological abnormalities increasing susceptibility to AF development. (4) Conclusion. Obesity may predispose to an overall increase in atrial electropathology, consisting of an increase in the slowing of the conduction, conduction block, low-voltage areas, and complex fractionated electrograms. To determine the impact of obesity-induced atrial electrical abnormalities on the long-term clinical outcome, further prospective studies are mandatory.

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Objective: Patients with persistent atrial fibrillation (AF) have more electrical endo-epicardial asynchrony (EEA) during sinus rhythm (SR) than patients without AF. Prior mapping studies indicated that particularly unipolar, endo- and/or epicardial electrogram (EGM) morphology may be indicators of EEA. This study aim to develop a novel method for estimating the degree of EEA by using unipolar EGM characteristics recorded from either the endo- and/or epicardium. Methods: Simultaneous endo-epicardial mapping during sinus rhythm was performed in 86 patients. EGM characteristics, including unipolar voltages, low-voltage areas (LVAs), potential types (single, short/long double and fractionated potentials: SP, SDP, LDP and FP) and fractionation duration (FD) of double potentials (DP) and FP were compared between EEA and non-EEA areas. Asynchrony Fingerprinting Scores (AFS) containing quantified EGM characteristics were constructed to estimate the degree of EEA. Results: Endo- and epicardial sites of EEA areas are characterized by lower unipolar voltages, a higher number of LDPs and FPs and longer DP and FP durations. Patients with AF have lower potential voltages in EEA areas, along with alterations in the potential types. The EE-AFS, containing the proportion of endocardial LVAs and FD of epicardial DPs, had the highest predictive value for determining the degree of EEA (AUC: 0.913). Endo- and epi-AFS separately also showed good predictive values (AUC: 0.901 and 0.830 respectively). Conclusions: EGM characteristics can be used to identify EEA areas. AFS can be utilized as a novel diagnostic tool for accurately estimating the degree of EEA. These characteristics potentially indicate AF related arrhythmogenic substrates.

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Background: Prior studies demonstrated that female sex is associated with arrhythmia recurrence after endovascular pulmonary vein isolation (PVI). However, it is unknown if the sexes differ in outcome after video assisted thoracoscopic (VATS) PVI. The aim of this study was therefore to compare characteristics of recurrent AF episodes in a matched male and female population, using implantable loop recorders for continuous rhythm monitoring. Methods: 40 matched (based on propensity score) males (age: 60.0 ± 7.71 (45–75)) and females (age: 62.0 ± 7.0 (37–74)) were retrieved from an existing database from a prior conducted study by the cardiothoracic department of the OLVG hospital (1) containing patients who received an implantable looprecorder and underwent a VATS PVI between 2012 and 2017. Patients were continuously monitored for a period of 12 months after VATS PVI and AF characteristics were compared. Results: An equal number of males and females had AF episodes during all periods (P > 0.05). The number of AF episodes was higher in females, during the first 6 months (P = 0.01, P = 0.034). During the entire follow up, the total AF duration was longer in females (P = 0.01, for all periods) with shorter inter - episode intervals (P = 0.001, P = 0001, P = 0.04) and a higher AF burden (P = 0.003, P = 0001, P = 0.006). After 3 months, AF recurrences during the night were more frequently observed in female patients (P = 0.001, P = 0.001). Conclusions: AF episodes occur frequently in both sexes after VATS PVI and warrant frequent rhythm monitoring. The observed sex differences in AF burden after VATS PVI, calls for intensive rhythm monitoring and aggressive treatment of recurrent AF epsiodes in females.

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Aims: Areas of conduction inhomogeneity (CI) during sinus rhythm may facilitate the initiation and perpetuation of atrial fibrillation (AF). Currently, no tool is available to quantify the severity of CI. Our aim is to develop and validate a novel tool using unipolar electrograms (EGMs) only to quantify the severity of CI in the atria. Methods and results: Epicardial mapping of the right atrium (RA) and left atrium, including Bachmann's bundle, was performed in 235 patients undergoing coronary artery bypass grafting surgery. Conduction inhomogeneity was defined as the amount of conduction block. Electrograms were classified as single, short, long double (LDP), and fractionated potentials (FPs), and the fractionation duration of non-single potentials was measured. The proportion of low-voltage areas (LVAs, <1mV) was calculated. Increased CI was associated with decreased potential voltages and increased LVAs, LDPs, and FPs. The Electrical Fingerprint Score consisting of RA EGM features, including LVAs and LDPs, was most accurate in predicting CI severity. The RA Electrical Fingerprint Score demonstrated the highest correlation with the amount of CI in both atria (r = 0.70, P < 0.001). Conclusion: The Electrical Fingerprint Score is a novel tool to quantify the severity of CI using only unipolar EGM characteristics recorded. This tool can be used to stage the degree of conduction abnormalities without constructing spatial activation patterns, potentially enabling early identification of patients at high risk of post-operative AF or selection of the appropriate ablation approach in addition to pulmonary vein isolation at the electrophysiology laboratory.

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BACKGROUND AND AIMS: Atrial extrasystoles (AES) provoke conduction disorders and may trigger episodes of atrial fibrillation (AF). However, the direction- and rate-dependency of electrophysiological tissue properties on epicardial unipolar electrogram (EGM) morphology is unknown. Therefore, this study examined the impact of spontaneous AES on potential amplitude, -fractionation, -duration, and low-voltage areas (LVAs), and correlated these differences with various degrees of prematurity and aberrancy. METHODS AND RESULTS: Intra-operative high-resolution epicardial mapping of the right and left atrium, Bachmann's Bundle, and pulmonary vein area was performed during sinus rhythm (SR) in 287 patients (60 with AF). AES were categorized according to their prematurity index (>25% shortening) and degree of aberrancy (none, mild/opposite, moderate and severe). In total, 837 unique AES (457 premature; 58 mild/opposite, 355 moderate, and 154 severe aberrant) were included. The average prematurity index was 28% [12-45]. Comparing SR and AES, average voltage decreased (-1.1 [-1.2, -0.9] mV, P < 0.001) at all atrial regions, whereas the amount of LVAs and fractionation increased (respectively, +3.4 [2.7, 4.1] % and +3.2 [2.6, 3.7] %, P < 0.001). Only weak or moderate correlations were found between EGM morphology parameters and prematurity indices (R2 < 0.299, P < 0.001). All parameters were, however, most severely affected by either mild/opposite or severely aberrant AES, in which the effect was more pronounced in AF patients. Also, there were considerable regional differences in effects provoked by AES. CONCLUSION: Unipolar EGM characteristics during spontaneous AES are mainly directional-dependent and not rate-dependent. AF patients have more direction-dependent conduction disorders, indicating enhanced non-uniform anisotropy that is uncovered by spontaneous AES.

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Estimating tissue conductivity parameters from electrograms (EGMs) could be an important tool for diagnosing and treating heart rhythm disorders such as atrial fibrillation (AF). One of these parameters is the fibre direction, often assumed to be known in conductivity estimation methods. In this paper, a novel method to estimate the fibre direction from EGMs is presented. This method is based on local conduction slowness vectors of a propagating activation wave. These conduction slowness vectors follow an elliptical pattern that depends on the underlying conductivity parameters. The fibre direction and conductivity anisotropy ratio can therefore be estimated by fitting an ellipse to the conduction slowness vectors. Applying the presented method on simulated data shows that it can estimate the fibre direction more accurately than existing methods, and that its performance depends mostly on the range of wavefront directions present in the measurement area. The main advantage of the presented method is that it still functions relatively well in the presence of conduction blocks, as long as the surrounding tissue is approximately homogeneous.@en

Background: Dominant frequencies (DFs) or complex fractionated atrial electrograms (CFAEs), indicative of focal sources or rotational activation, are used to identify target sites for atrial fibrillation (AF) ablation in clinical studies, although the relationship among DF, CFAE, and activation patterns remains unclear. Objectives: This study sought to investigate the relationship between patterns of activation underlying DF and CFAE sites during AF. Methods: Epicardial high-resolution mapping of the right and left atrium including Bachmann's bundle was performed in 71 participants. We identified the highest dominant frequency (DF _max) and highest degree of CFAE (CFAE _max) with the use of existing clinical criteria and classified patterns of activation as focal or rotational activation and smooth propagation, conduction block (CB), collision and remnant activity, and fibrillation potentials as single, double, or fractionated potentials containing, respectively, 1, 2, or 3 or more negative deflections. Relationships among activation patterns, DF _max, and potential types were investigated. Results: DF _max were primarily located at the left atrioventricular groove and did not harbor focal activation (proportion focal waves: 0% [IQR: 0%-2%]). Compared with non-DF _max sites, DF _max were characterized by more frequent smooth propagation (22% [IQR: 7%-48%] vs 17% [IQR: 11%-24%]; P = 0.001), less frequent conduction block (69% [IQR: 51%-81%] vs 74% [IQR: 69%-78%]; P = 0.006), a higher proportion of single potentials (72% [IQR: 55%-84%] vs 6%1 [IQR: 55%-65%]; P = 0.003), and a lower proportion of fractionated potentials (4% [IQR: 1%-11%] vs 12% [IQR: 9%-15%]; P = 0.004). CFAE _max were mainly found at the pulmonary veins area, and only 1% [IQR: 0%-2%] of all CFAE _max contained focal activation. Compared with non-CFAE _max sites, CFAE _max sites were characterized by less frequent smooth propagation (1% [IQR: 0%-1%] vs 17% [IQR: 12%-24%]; P < 0.001) and more frequent remnant activity (20% [IQR: 12%-29%] vs 8% [IQR: 5%-10%]; P < 0.001), and harbored predominantly fractionated potentials (52% [IQR: 43%-66%] vs 12% [IQR: 9%-14%]; P < 0.001). Conclusions: Focal or rotational patterns of activation were not consistently detected at DF _max domains and CFAE _max sites. These findings do not support the concept of targeting DF _max or CFAE _max according to existing criteria for AF ablation.

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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

Background: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and reliable method to train a classifier for AF detection using large datasets of real-life ECGs. Method: Human-controlled semi-supervised learning was applied, consisting of two phases: the pre-training phase and the semi-automated training phase. During pre-training, an initial classifier was trained, which was used to predict the classes of new ECG segments in the semi-automated training phase. Based on the degree of certainty, segments were added to the training dataset automatically or after human validation. Thereafter, the classifier was retrained and this procedure was repeated. To test the model performance, a real-life telemetry dataset containing 3,846,564 30-s ECG segments of hospitalized patients (n = 476) and the CinC Challenge 2017 database were used. Results: After pre-training, the average F1-score on a hidden testing dataset was 89.0%. Furthermore, after the pre-training phase 68.0% of all segments in the hidden test set could be classified with an estimated probability of successful classification of 99%, providing an F1-score of 97.9% for these segments. During the semi-automated training phase, this F1-score showed little variation (97.3%–97.9% in the hidden test set), whilst the number of segments which could be automatically classified increased from 68.0% to 75.8% due to the enhanced training dataset. At the same time, the overall F1-score increased from 89.0% to 91.4%. Conclusions: Human-validated semi-supervised learning makes training a classifier more time efficient without compromising on accuracy, hence this method might be valuable in the automated detection of AF in real-life ECGs.

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Mathematical models of the electrophysiology of cardiac tissue play an important role when studying heart rhythm disorders like atrial fibrillation. Model parameters such as conductivity, activation time, and anisotropy ratio are useful parameters to determine the arrhythmogenic substrate that causes abnormalities in the atrial tissue. Existing methods often estimate the model parameters separately and assume some of the parameters to be known as a priori knowledge. In this work, we propose an efficient method to jointly estimate the parameters of interest from the cross power spectral density matrix (CPSDM) model of the electrograms. By applying confirmatory factor analysis (CFA) to the CPSDMs of multi-electrode electrograms, we can make use of the spatial information of the data and analyze the relationship between the desired resolution and the required amount of data. With the reasonable assumptions that the conductivity parameters and the anisotropy parameters are constant across different frequencies and heart beats, we estimate these parameters using multiple frequencies and multiple heart beats simultaneously to easier satisfy the identifiability conditions in the CFA problem. Results on the simulated data show that using multiple heart beats decreases the estimation errors of the conductivity and the estimated activation time parameters. The experimental results on clinical data show that using multiple heart beats for parameter estimation can reduce the reconstruction errors of the clinical electrograms, which further demonstrates the robustness of the proposed method.

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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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We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter-electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

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