Vector Field Heterogeneity for the Assessment of Locally Disorganised Cardiac Electrical Propagation Wavefronts From High-Density Multielectrodes.

High-density multielectrode catheters are becoming increasingly popular in cardiac electrophysiology for advanced characterisation of the cardiac tissue, due to their potential to identify impaired sites. These are often characterised by abnormal electrical conduction, which may cause locally disorganised propagation wavefronts. To quantify it, a novel heterogeneity parameter based on vector field analysis is proposed, utilising finite differences to measure direction changes between adjacent cliques. The proposed Vector Field Heterogeneity metric has been evaluated on a set of simulations with controlled levels of organisation in vector maps, and a variety of grid sizes. Furthermore, it has been tested on animal experimental models of isolated Langendorff-perfused rabbit hearts. The proposed parameter exhibited superior capturing ability of heterogeneous propagation wavefronts compared to the classical Spatial Inhomogeneity Index, and simulations proved that the metric effectively captures gradual increments in disorganisation in propagation patterns. Notably, it yielded robust and consistent outcomes for [Formula: see text] grid sizes, underscoring its suitability for the latest generation of orientation-independent cardiac catheters.

Robust Framework for Medical Time Series Classification and Application to Real Scenarios in Modern Bioengineering.

In this study, a novel unsupervised classification framework for time series of medical nature is presented. This framework is based on the intersection of machine learning, Hilbert Spaces algebra, and signal theory. The methodology is illustrated through the resolution of three biomedical engineering problems: neuronal activity tracking, protein functional classification, and non-invasive diagnosis of atrial flutter (AFL). The results indicate that the proposed algorithms exhibit high proficiency in solving these tasks and demonstrate robustness in identifying damaged neuronal units while tracking healthy ones. Moreover, the application of the framework in protein functional classification provides a new perspective for the development of pharmaceutical products and personalised medicine. Additionally, the controlled environment of the framework in AFL simulation problem underscores the algorithm's ability to encode information efficiently. These results offer valuable insights into the potential of this framework and lay the groundwork for future studies.Clinical relevance- The framework proposed in this study has the potential to yield novel insights into the effects of newly implanted electrodes in the brain. Furthermore, the categorization of proteins by function could facilitate the development of personalised and efficient medicines, ultimately reducing both time and cost. The simulation of atrial flutter also demonstrates the framework's ability to encode information for arrhythmia diagnosis and treatment, which has the potential to lead to improved patient outcomes.

Mini Peltier Cell Array System for the Generation of Controlled Local Epicardial Heterogeneities.

The present study aims to design and fabricate a system capable of generating heterogeneities on the epicardial surface of an isolated rabbit heart perfused in a Langendorff system. The system consists of thermoelectric modules that can be independently controlled by the developed hardware, thereby allowing for the generation of temperature gradients on the epicardial surface, resulting in conduction slowing akin to heterogeneities of pathological origin. A comprehensive analysis of the system's viability was performed through modeling and thermal simulation, and its practicality was validated through preliminary tests conducted at the experimental cardiac electrophysiology laboratory of the University of Valencia. The design process involved the use of Fusion 360 for 3D designs, MATLAB/Simulink for algorithms and block diagrams, LTSpice and Altium Designer for schematic captures and PCB design, and the integration of specialized equipment for animal experimentation. The objective of the study was to efficiently capture epicardial recordings under varying conditions.Clinical relevance- The proposed system aims to induce local epicardial heterogeneities to generate labeled correct signals that can serve as a golden standard for improving algorithms that identify and characterize fibrotic substrates. This improvement will enhance the efficacy of ablation processes and potentially reduce the ablated surface area.

Evaluation and assessment of clique arrangements for the estimation of omnipolar electrograms in high density electrode arrays: an experimental animal model study.

High-density catheters combined with Orientation Independent Sensing (OIS) methods have emerged as a groundbreaking technology for cardiac substrate characterisation. In this study, we aim to assess the arrangements and constraints to reliably estimate the so-called omnipolar electrogram (oEGM). Performance was evaluated using an experimental animal model. Thirty-eight recordings from nine retrospective experiments on isolated perfused rabbit hearts with an epicardial HD multielectrode were used. We estimated oEGMs according to the classic triangular clique (4 possible orientations) and a novel cross-orientation clique arrangement. Furthermore, we tested the effects of interelectrode spacing from 1 to 4 mm. Performance was evaluated by means of several parameters that measured amplitude rejection ratios, electric field loop area, activation pulse width and morphology distortion. Most reliable oEGM estimations were obtained with cross-configurations and interelectrode spacings [Formula: see text] mm. Estimations from triangular cliques resulted in wider electric field loops and unreliable detection of the direction of the propagation wavefront. Moreover, increasing interelectrode distance resulted in increased pulse width and morphology distortion. The results prove that current oEGM estimation techniques are insufficiently accurate. This study opens a new standpoint for the design of new-generation HD catheters and mapping software.

Performance assessment of electrode configurations for the estimation of omnipolar electrograms from high density arrays.

OBJECTIVE: The aim of this study is to propose a method to reduce the sensitivity of the estimated omnipolar electrogram (oEGM) with respect to the angle of the propagation wavefront. METHODS: A novel configuration of cliques taking into account all four electrodes of a squared cell is proposed. To test this approach, simulations of HD grids of cardiac activations at different propagation angles, conduction velocities, interelectrode distance and electrogram waveforms are considered. RESULTS: The proposed approach successfully provided narrower loops (essentially a straight line) of the electrical field described by the bipole pair with respect to the conventional approach. Estimation of the direction of propagation was improved. Additionally, estimated oEGMs presented larger amplitude, and estimations of the local activation times were more accurate. CONCLUSIONS: A novel method to improve the estimation of oEGMs in HD grid of electrodes is proposed. This approach is superior to the existing methods and avoids pitfalls not yet resolved. RELEVANCE: Robust tools for quantifying the cardiac substrate are crucial to determine with accuracy target ablation sites during an electrophysiological procedure.

From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy.

Objective. Detecting different cardiac diseases using a single or reduced number of leads is still challenging. This work aims to provide and validate an automated method able to classify ECG recordings. Performance using complete 12-lead systems, reduced lead sets, and single-lead ECGs is evaluated and compared.Approach. Seven different databases with 12-lead ECGs were provided during thePhysioNet/Computing in Cardiology Challenge2021, where 88 253 annotated samples associated with none, one, or several cardiac conditions among 26 different classes were released for training, whereas 42 896 hidden samples were used for testing. After signal preprocessing, 81 features per ECG-lead were extracted, mainly based on heart rate variability, QRST patterns and spectral domain. Next, a One-versus-Rest classification approach made of independent binary classifiers for each cardiac condition was trained. This strategy allowed each ECG to be classified as belonging to none, one or several classes. For each class, a classification model among two binary supervised classifiers and one hybrid unsupervised-supervised classification system was selected. Finally, we performed a 3-fold cross-validation to assess the system's performance.Main results. Our classifiers received scores of 0.39, 0.38, 0.39, 0.38, and 0.37 for the 12, 6, 4, 3 and 2-lead versions of the hidden test set with the Challenge evaluation metric (CM). Also, we obtained a meanG-score of 0.80, 0.78, 0.79, 0.79, 0.77 and 0.74 for the 12, 6, 4, 3, 2 and 1-lead subsets with the public training set during our 3-fold cross-validation.Significance. We proposed and tested a machine learning approach focused on flexibility for identifying multiple cardiac conditions using one or more ECG leads. Our minimal-lead approach may be beneficial for novel portable or wearable ECG devices used as screening tools, as it can also detect multiple and concurrent cardiac conditions.

Estimation of PQ distance dispersion for atrial fibrillation detection.

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. It is associated with significantly increased morbidity and mortality. Diagnosis of the disease can be based on the analysis of the electrical atrial activity, on quantification of the heart rate irregularity or on a mixture of the both approaches. Since the amplitude of the atrial waves is small, their analysis can lead to false results. On the other hand, the heart rate based analysis usually leads to many unnecessary warnings. Therefore, our goal is to develop a new method for effective AF detection based on the analysis of the electrical atrial waves. METHODS: The proposed method employs the fact that there is a lack of repeatable P waves preceding QRS complexes during AF. We apply the operation of spatio-temporal filtering (STF) to magnify and detect the prominent spatio-temporal patterns (STP) within the P waves in multi-channel ECG recordings. Later we measure their distances (PQ) to the succeeding QRS complexes, and we estimate dispersion of the obtained PQ series. For signals with normal sinus rhythm, this dispersion is usually very low, and contrary, for AF it is much raised. This allows for effective discrimination of this cardiologic disorder. RESULTS: Tested on an ECG database consisting of AF cases, normal rhythm cases and cases with normal rhythm restored by the use of cardioversion, the method proposed allowed for AF detection with the accuracy of 98.75% on the basis of both 8-channel and 2-channel signals of 12 s length. When the signals length was decreased to 6 s, the accuracy varied in the range of 95%-97.5% depending on the number of channels and the dispersion measure applied. CONCLUSIONS: Our approach allows for high accuracy of atrial fibrillation detection using the analysis of electrical atrial activity. The method can be applied to an early detection of the desease and can advantageously be used to decrease the number of false warnings in systems based on the analysis of the heart rate.

Non-invasive characterisation of macroreentrant atrial tachycardia types from a vectorcardiographic approach with the slow conduction region as a cornerstone.

BACKGROUND AND OBJECTIVES: Macroreentrant atrial tachyarrhythmias (MRATs) can be caused by different reentrant circuits. The treatment for each MRAT type may require ablation at different sites, either at the right or left atria. Unfortunately, the reentrant circuit that drives the arrhythmia cannot be ascertained previous to the electrophysiological intervention. METHODS: A noninvasive approach based on the comparison of atrial vectorcardiogram (VCG) loops is proposed. An archetype for each group was created, which served as a reference to measure the similarity between loops. Methods were tested in a variety of simulations and real data obtained from the most common right (peritricuspid) and left (perimitral) macroreentrant circuits, each divided into clockwise and counterclockwise subgroups. Adenosine was administered to patients to induce transient AV block, allowing the recording of the atrial signal without the interference of ventricular signals. From the vectorcardiogram, we measured intrapatient loop consistence, similarity of the pathway to archetypes, characterisation of slow velocity regions and pathway complexity. RESULTS: Results show a considerably higher similarity with the loop of its corresponding archetype, in both simulations and real data. We found the capacity of the vectorcardiogram to reflect a slow velocity region, consistent with the mechanisms of MRAT, and the role that it plays in the characterisation of the reentrant circuit. The intra-patient loop consistence was over 0.85 for all clinical cases while the similarity of the pathway to archetypes was found to be 0.85 ± 0.03, 0.95 ± 0.03, 0.87 ± 0.04 and 0.91 ± 0.02 for the different MRAT types (and p<0.02 for 3 of the 4 groups), and pathway complexity also allowed to discriminate among cases (with p<0.05). CONCLUSIONS: We conclude that the presented methodology allows us to differentiate between the most common forms of right and left MRATs and predict the existence and location of a slow conduction zone. This approach may be useful in planning ablation procedures in advance.

Automated Atrial Fibrillation Detection by ECG Signal Processing: A Review.

Cardiovascular diseases are the main cause of death in the world, according to the World Health Organization. Among them, ischemic heart disease is at the top, followed by a stroke. Several studies have revealed that atrial fibrillation (AF), which is the most common cardiac arrhythmia, increases up to five fold the overall risk of stroke. As AF can be asymptomatic, approximately 20% of the AF cases remain undiagnosed. AF can be detected by analyzing electrocardiography records. Many studies have been conducted to develop automatic methods for AF detection. This paper reviews some of the most relevant methods, classified into three groups: analysis of heart rate variability, analysis of the atrial activity, and hybrid methods. Their benefits and limitations are analyzed and compared, and our beliefs about where AF automatic detection research could be addressed are presented to improve its effectiveness and performance.

Classification model based on strain measurements to identify patients with arrhythmogenic cardiomyopathy with left ventricular involvement.

BACKGROUND AND OBJECTIVE: A heterogenous expression characterizes arrhythmogenic cardiomyopathy (AC). The evaluation of regional wall movement included in the current Task Force Criteria is only qualitative and restricted to the right ventricle. However, a strain-based approach could precisely quantify myocardial deformation in both ventricles. We aim to define and modelize the strain behavior of the left ventricle in AC patients with left ventricular (LV) involvement by applying algorithms such as Principal Component Analysis (PCA), clustering and naïve Bayes (NB) classifiers. METHODS: Thirty-six AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine cardiac magnetic resonance imaging to assess strain time series from a 3D approach, to which PCA was applied. A Two-Step clustering algorithm separated the patients' group into clusters according to their level of LV strain impairment. A statistical characterization between controls and the new AC subgroups was done. Finally, a NB classifier was built and new data from a small evolutive dataset was predicted. RESULTS: 60% of AC-LV patients showed mildly affected strain and 40% severely affected strain. Both groups and controls exhibited statistically significant differences, especially when comparing controls and severely affected AC-LV patients. The classification accuracy of the strain NB classifier reached 82.76%. The model performance was as good as to classify the individuals with a 100% sensitivity and specificity for severely impaired strain patients, 85.7% and 81.1% for mildly impaired strain patients, and 69.9% and 91.4% for normal strain, respectively. Even when the severely affected LV-AC group was excluded, LV strain showed a good accuracy to differentiate patients and controls. The prediction of the evolutive dataset revealed a progressive alteration of strain in time. CONCLUSIONS: Our LV strain classification model may help to identify AC patients with LV involvement, at least in a setting of a high pretest probability, such as family screening.

Left ventricular myocardial dysfunction in arrhythmogenic cardiomyopathy with left ventricular involvement: A door to improving diagnosis.

BACKGROUND: Diagnostic Task Force Criteria (TFC) for arrhythmogenic cardiomyopathy (AC) exhibit poor performance for left dominant forms. TFC only include right ventricular (RV) dysfunction (akinesia, dyssynchrony, volumes and ejection fraction). Moreover, cardiac magnetic resonance imaging (CMRI) assessment of left ventricular (LV) dyssynchrony has hitherto not been described. Thus, we aimed to comprehensively characterize LV CMRI behavior in AC patients. METHODS: Thirty-five AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine CMRI to assess LV ejection fraction (LVEF), LV end-systolic and end-diastolic volume indexes, strain values and dyssynchrony. Regions with more frequent strain and dyssynchrony impairment were also studied. RESULTS: Radial dyssynchrony and LVEF were selected (sensitivities 54.3% and 48.6%, respectively at 100% specificity), with a threshold of 70 ms for radial dyssynchrony and 48.5% for LVEF. 71.4% of patients exceeded these thresholds (31.4% both, 22.9% only dyssynchrony and 17.1% only LVEF). Considering these cut-off values as a novel combined criterion, 30% of patients with 'borderline' or 'possible' AC following 2010 TFC would move to a 'definite' AC diagnosis. Strain was globally impaired whereas dyssynchronous regions were more often apical and located at the inferolateral wall. CONCLUSIONS: Mirroring the RV evaluation, we suggest including LVEF and LV dyssynchrony to improve the diagnosis of AC. Two independent mechanisms can be claimed in AC patients with LV involvement: 1) decreased myocardial deformation with global LV affectation and 2) delayed myocardial contraction at localized regions.

Granger Causality and Jensen-Shannon Divergence to Determine Dominant Atrial Area in Atrial Fibrillation.

Atrial fibrillation (AF) is already the most commonly occurring arrhythmia. Catheter pulmonary vein ablation has emerged as a treatment that is able to make the arrhythmia disappear; nevertheless, recurrence to arrhythmia is very frequent. In this study, it is proposed to perform an analysis of the electrical signals recorded from bipolar catheters at three locations, pulmonary veins and the right and left atria, before to and during the ablation procedure. Principal Component Analysis (PCA) was applied to reduce data dimension and Granger causality and divergence techniques were applied to analyse connectivity along the atria, in three main regions: pulmonary veins, left atrium (LA) and right atrium (RA). The results showed that, before the procedure, patients with recurrence in the arrhythmia had greater connectivity between atrial areas. Moreover, during the ablation procedure, in patients with recurrence in the arrhythmial both atria were more connected than in patients that maintained sinus rhythms. These results can be helpful for procedures designing to end AF.

Refined Multiscale Fuzzy Entropy to Analyse Post-Exercise Cardiovascular Response in Older Adults With Orthostatic Intolerance.

Orthostatic intolerance syndrome occurs when the autonomic nervous system is incapacitated and fails to respond to the demands associated with the upright position. Assessing this syndrome among the elderly population is important in order to prevent falls. However, this problem is still challenging. The goal of this work was to determine the relationship between orthostatic intolerance (OI) and the cardiovascular response to exercise from the analysis of heart rate and blood pressure. More specifically, the behavior of these cardiovascular variables was evaluated in terms of refined composite multiscale fuzzy entropy (RCMFE), measured at different scales. The dataset was composed by 65 older subjects, 44.6% (n = 29) were OI symptomatic and 55.4% (n = 36) were not. Insignificant differences were found in age and gender between symptomatic and asymptomatic OI participants. When heart rate was evaluated, higher differences between groups were observed during the recovery period immediately after exercise. With respect to the blood pressure and other hemodynamic parameters, most significant results were obtained in the post-exercise stage. In any case, the symptomatic OI group exhibited higher irregularity in the measured parameters, as higher RCMFE levels in all time scales were obtained. This information could be very helpful for a better understanding of cardiovascular instability, as well as to recognize risk factors for falls and impairment of functional status.

An open access database for the evaluation of heart sound algorithms.

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

Entropy at the right atrium as a predictor of atrial fibrillation recurrence outcome after pulmonary vein ablation.

Atrial fibrillation (AF) recurrence rates after successful ablation procedures are still high and difficult to predict. This work studies the capability of entropy measured from intracardiac recordings as an indicator for recurrence outcome. Intra-atrial recordings from 31 AF patients were registered previously to an ablation procedure. Four electrodes were located at the right atrium (RA) and four more at the left atrium (LA). Sample entropy measurements were applied to these signals, in order to characterize different non-linear AF dynamics at the RA and LA independently. In a 3 months follow-up, 19 of them remained in sinus rhythm, whereas the other 12 turned back to AF. Entropy values can be associated to a proarrhythmic indicator as they were higher in patients with AF recurrence (1.11±0.15 vs. 0.91±0.13), in persistent patients (1.03±0.19 vs. 0.96±0.15), and at the LA with respect to the RA (1.03±0.23 vs. 0.89±0.15 for paroxysmal AF patients). Furthermore, entropy values at the RA arose as a more reliable predictor for recurrence outcome than at the LA. Results suggest that high entropy values, especially at the RA, are associated with high risk of AF recurrence. These findings show the potential of the proposed method to predict recurrences post-ablation, providing additional insights to the understanding of arrhythmia.

Spectral analysis-based risk score enables early prediction of mortality and cerebral performance in patients undergoing therapeutic hypothermia for ventricular fibrillation and comatose status.

BACKGROUND: Early prognosis in comatose survivors after cardiac arrest due to ventricular fibrillation (VF) is unreliable, especially in patients undergoing mild hypothermia. We aimed at developing a reliable risk-score to enable early prediction of cerebral performance and survival. METHODS: Sixty-one out of 239 consecutive patients undergoing mild hypothermia after cardiac arrest, with eventual return of spontaneous circulation (ROSC), and comatose status on admission fulfilled the inclusion criteria. Background clinical variables, VF time and frequency domain fundamental variables were considered. The primary and secondary outcomes were a favorable neurological performance (FNP) during hospitalization and survival to hospital discharge, respectively. The predictive model was developed in a retrospective cohort (n = 32; September 2006-September 2011, 48.5 ± 10.5 months of follow-up) and further validated in a prospective cohort (n = 29; October 2011-July 2013, 5 ± 1.8 months of follow-up). RESULTS: FNP was present in 16 (50.0%) and 21 patients (72.4%) in the retrospective and prospective cohorts, respectively. Seventeen (53.1%) and 21 patients (72.4%), respectively, survived to hospital discharge. Both outcomes were significantly associated (p < 0.001). Retrospective multivariate analysis provided a prediction model (sensitivity = 0.94, specificity = 1) that included spectral dominant frequency, derived power density and peak ratios between high and low frequency bands, and the number of shocks delivered before ROSC. Validation on the prospective cohort showed sensitivity = 0.88 and specificity = 0.91. A model-derived risk-score properly predicted 93% of FNP. Testing the model on follow-up showed a c-statistic ≥ 0.89. CONCLUSIONS: A spectral analysis-based model reliably correlates time-dependent VF spectral changes with acute cerebral injury in comatose survivors undergoing mild hypothermia after cardiac arrest.

On the preprocessing of atrial electrograms in atrial fibrillation: understanding Botteron's approach.

BACKGROUND: The dominant atrial frequency is a key parameter for the analysis of atrial fibrillation (AF) from intracardiac recordings. The preprocessing approach employed by Botteron et al. in an early work is able to retrieve this frequency. The preprocessing steps are: (1) 40-250-Hz band-pass filtering, (2) rectification, and (3) 20 Hz low-pass filtering. METHODS AND RESULTS: The theoretical aspects of this process are addressed. Moreover, its time-domain and frequency-domain properties are evaluated using both simulations and real electrogram (EGM) recordings. The fundamental frequency is emphasized, due to the rectification step. As the interval between consecutive activations becomes more irregular, fundamental frequency detection becomes less robust. In the case of fractionated EGM, this approach fails. In time-domain, the waveform of the atrial beats are dramatically modified, hence hindering any further analysis on the morphology of the activations. CONCLUSIONS: Botteron preprocessing succeeds in estimating the dominant atrial rate in most EGMs during AF. However, this approach presents some limitations and improved methods are required.

Profound sedation with propofol modifies atrial fibrillation dynamics.

During atrial fibrillation (AF), multiple wandering propagation wavelets at high rates drift around both atria under controversial hierarchical models. Antiarrhythmic drugs modify the cardiac ionic currents supporting the fibrillation process within the atria, and can alter AF propagation dynamics and even terminate the arrhythmia. However, some other drugs, theoretically nonantiarrhythmic, may slightly block particular cardiac ionic currents through uncertain mechanisms in such a subtle way at regular heart rates that may have been pharmacologically overlooked. These potential effects might be better exposed at much higher activation rates as in AF, where atrial cells depolarize over 400 times per second. In this review, we aimed to compile and discuss results from several studies evaluating the net effect of profound sedation with propofol on atrial cells and atrioventricular (AV) conduction. Propofol is a very commonly used anesthetic agent, and its possible effect on AF dynamics has systematically not been taken into account in the myriad of clinical studies dealing with AF intracardiac recordings. The possible effect of sedation with propofol on AF was evaluated through the analysis of AF propagation patterns before and after its infusion in a series of patients submitted to pulmonary vein ablation. Effect on AV conduction will be discussed as well.

Morphology quantification of atrial fibrillation under propofol using principal components analysis.

The effect of the most useful anaesthetic in cardiovascular therapy, called propofol, was evaluated in patients with atrial fibrillation (AF). In order to represent the electrogram recordings in a more efficient manner Principal component analysis (PCA) has been applied. Moreover, to reduce the data set to a few representative activations PCA was applied again to measure the average dissimilarity between consecutive activations of an intracardiac signal. In addition PCA was applied directly to the activations extracted from each dipole to analysis temporal varibility, and spatial variability with the comparation of the signals from different dipoles. The proposed indexes show different behaviour patterns along the atrial area during the anaesthetic effects.

Topographic imaging of the atrial electrical activity during atrial fibrillation for the analysis of uniform distributions of the surface electrical potentials.

Atrial fibrillation (AF) is a progressive arrhythmia which causes time dependent impairing of the cardiac muscle. This makes that proper therapeutic interventions depend on the degree of AF progression, i.e., on the temporal decrease of the organization of the electrical patterns observed during AF. Standard effective treatments are still lacking nowadays, and this calls for suitable noninvasive analysis of AF. In this sense, an appropriate therapy relies on the knowledge of AF characteristics, as its degree of organization. To this purpose, fast and accurate imaging of cardiac electrical activity can be helpful. Relying on the results of previous work on noninvasive assessment of the complexity of AF, we put forward a method to obtain visual maps of the topographic projection of the main atrial activity (AA) component given by principal component analysis, which is shown to provide detailed information about AA potential pattern distributions on the body surface. Different AA potential pattern distributions can then be identified, depending on the underlying degree of AF organization. An automated way to assess AF organization degree is then proposed, based on topographic projections. Similarities with previous studies suggest its usefulness for determining uniform distributions in the activation patterns on the body surface.