Patient-specific three-dimensional torso models for analysing cardiac activity

Standard electrocardiogram (ECG) is routinely used for recording cardiac electrical activity but lacks 3D details. Body surface potential mapping techniques have been developed more recently. A major challenge related to this technique is the projection of body signals back to their original sources in the heart. For an accurate projection, one needs to take into account patient-specific electrophysiological data of tissues surrounding the heart. Much information regarding physiological variations, as well as the exact position of organs in the torso, can be obtained from magnetic resonance (MR) images. Here, a patient-specific methodology for building 3D torso models from transverse MR images is proposed. Torso contour detection is based on edge detection using a canny filter and indicating contour points with a polar coordinate system. Organ detection is performed using an interpolation technique with Active Contour modelling. Results show that accurate torso models can be constructed with short processing time

Visualizing intracardiac atrial fibrillation electrograms using spectral analysis

Atrial fibrillation is the most common cardiac arrhythmia, and it is associated with increased risk of stroke, heart failure, and mortality. This work describes spectral analysis techniques that are being used in conjunction with visualization algorithms to help guide catheter ablation procedures that aim at treating patients with arrhythmia

A platform to guide catheter ablation of persistent atrial fibrillation using dominant frequency mapping

Dominant frequency (DF) mapping has been widely used to study the pathophysiology of atrial fibrillation (AF). In this study, a DF mapping system was developed to guide catheter ablation on electro-physiology (EP) procedures of persistent AF patients. The proposed platform has an automated graphical user interface (GUI) that processes non-contact unipolar electrograms (EGMs) recorded simultaneously by St. Jude Ensite Velocity System and provides 3D representation of the left atrium with DF behaviours and phase analysis

Are atrial fibrillation highest dominant frequency (HDF) areas the source of dominant excitation patterns? A left atrial panoramic view

Atrial fibrillation (AF) catheter ablation success depends on the possibility to accurately determine areas on the atrial endocardium at which AF activation originates. One way to determine if major AF activation pathways originate at identified source is through causality analysis. This work assessed to what extent left atrial highest dominant frequency (HDF) areas can be identified as sources of activation pathways in 10 male subjects suffering from persistent AF. Virtual electrograms were collected from 64 endocardial locations for at least 5 minutes. Frequency and causality were analyzed on 4 s signal segments Causality was assessed using the directed transfer function (DTF) algorithm, and AF activation sources were identified as endocardial locations of which the VEGM signal had high influence on other VEGM signals. Co-localization of high influence and HDF areas was evaluated for different area overlap and spectral organisation (OI) thresholds. Results show that, on average, good overlap only existed in 64.6% (± 8.8%) over all subject using the lowest threshold settings. Good overlap rates reduced with more conservative thresholds. This indicates that HDF areas might not always identify origins of main AF activation pathways

Unifying automated fractionated atrial electrogram classification using electroanatomical mapping systems in persistent atrial fibrillation studies

Ablation targeting complex fractionated atrial electrograms (CFAE) for treating persistent atrial fibrillation (persAF) has shown conflicting results. Differences in automated algorithms embedded in NavX (St Jude Medical) and CARTO (Biosense Webster) could influence CF AE target identification for ablation, potentially affecting ablation outcomes. To evaluate this effect, automated CFAE classification performed by NavX and CARTO on the same bipolar electrograms from 18 persAF patients undergoing ablation was compared. Using the default thresholds, NavX classified 69±5% of the electrograms as CFAEs, while CARTO detected 35±5%% (Cohen's kappa κ≈0.3, P<0.0001). Both primary and complementary metrics for each system were optimized to balance CF AE detection for both systems. Using revised thresholds found from receiver operating characteristic curves, NavX classified 45±4%, while CARTO detected 42±5% (κ≈0.5, P<0.0001). Our work takes a first step towards the optimization of CFAE detection between NavX and CARTO by providing revised thresholds to reduce differences in CF AE classification. This would facilitate direct comparisons of persAF CFAE-guided ablation outcome guided by NavX or CARTO

Investigation on recurrent high dominant frequency spatiotemporal patterns during persistent atrial fibrillation

Atrial regions hosting dominant frequency (DF) may represent potential drivers of persistent atrial fibrillation (persAF). Previous work showed that DF can exhibit cyclic behaviour. This study aims to better understand the spatiotemporal behaviours of persAF over longer time periods. 10 patients undergoing persAF ablation targeted at DF were included. Left atrial (LA) non-contact virtual electrograms (VEGMs, Ensite Array, St Jude Medical) were collected for up to 5 min pre-/post- ablation. DF was identified as the peak from 4-10 Hz, in 4 s windows (50 % overlap). High DF (HDF) map was created and automated pattern recognition algorithm was applied to look for recurring HDF spatial patterns within each patient. Recurring HDF patterns were found in all patients. Patients who changed rhythm to atrial flutter after ablation demonstrated single dominant pattern (DP) among the recorded time period, which might consistent with the higher level of regularity during flutter. Ablation regularized AF as demonstrated by increased DP recurrence after ablation. The time interval (median [IQR]) of DP recurrence for the patients still in atrial fibrillation(AF) after ablation (7 patients) decreased from 21.1 s [11.8~49.7 s] to 15.7s [6.5~18.2 s]. The proposed method quantifies the spatiotemporal regularity of HDF DPs over long time periods and may offer a more comprehensive dynamic overview of persAF behaviour and the impact of ablation

Combination of frequency and phase to characterise the spatiotemporal behaviour of cardiac waves during persistent atrial fibrillation in humans

The spatial distribution of atrial dominant frequency (DF), phase and phase singularity points (PSs) may reflect mechanisms driving and maintaining persistent atrial fibrillation (persAF). Here we developed an automatic algorithm that combines the three parameters and depicts the complex spatiotemporal patterns of fibrillation. For 9 patients undergoing left atrial persAF ablation, noncontact virtual unipolar electrograms (VEGMs) were simultaneously collected using a balloon array (Ensite Velocity, St. Jude Medical). After removal of the far field ventricular influence, we used fast Fourier transform and Hilbert transform to detect the DF and phase of each VEGM PSs are detected by finding the curl of the spatial phase gradient. DF along with phase and PSs were plotted for each window and the behaviour of the trajectory of HDF 'clouds' was observed. Our results indicate that spatial and temporal organization correlating HDF and phase exists during persAF. Generating and analysing the maps of HDF and phase may prove helpful in understanding the spatial and temporal activation dynamics during persAF

Atrial fibrillation patterns are pre-processing method-based dependent

Background, Motivation and Objective.Atrial fibrillation (AF) is the most common arrhythmia in clinical practice and affects about 0.5% of the worldwide population. It causes significant morbidity and increased mortality with sufferers experiencing fast, irregular heartbeats or palpitation, breathlessness, dizziness or even blackouts and is considered the biggest factor of risk for cerebral vascular accidents. Once AF is initiated, dynamic alterations of atrial electrophysiological properties occur invoking, in turn, AF inducibility. In patients for whom AF persists for long-term periods (persAF), identification of critical areas for successful ablation remains a challenge. Dominant frequency (DF) ablation resulted in interatrial DF gradient reduction, prolonging patient’s sinus rhythm. In addition, high-density DF mapping of persAF allowed recognition of dynamic spatio-temporal patterns, suggesting that ablation therapy is unlikely to be favoured by observing a single time frame. Investigators identified AF re-entry sources using phase analysis techniques in invasive and non-invasive electrophysiology systems. They also showed that targeting these sources appears to favour treatment success. Improving our understanding of the underlying AF behaviour is a key factor to contribute towards improving patient outcome. Modern commercial equipment has been developed to help clinicians identify and target patient-specific mechanisms responsible for AF maintenance, increasing patients’ successful treatment rates. On the other hand, the systems’ intrinsic methods limitations (number of electrodes and signal processing) have contributed to conflicting results on the fibrillatory mechanism. This study aims at analysing the impact of the pre-processing approaches currently used by commercial systems and clinical studies on the patients’ phase maps outcomes.Methods.2048 simultaneous unipolar intracardiac electrograms (AEGs) with duration of 4 s were collected in the left atrium(LA) (Noncontact Mapping, EnSite System, St. Jude) of 4 patients with persAF before and after Pulmonary Vein Isolation (PVI)(Sampling frequency, Fs=1200 Hz). Firstly, cancellation of the ventricular influence on the AEGs was performed followed by other pre-processing steps where six different band-pass strategies were implemented: 1-150 Hz, 1-50 Hz, 1-15 Hz, 3-50 Hz, 3-15 Hz and at the highest dominant frequency (HDF, band of ±1 Hz) identified on the respective 3D DF map. The band-pass filters were implemented with a forward-backward Butterworth filter of order 10. Prior applying the high-pass filter, the AEGs were down sampled(fs= 40 Hz) and filtered and then up sampled to the original sampling frequency (1200 Hz). After, the AEGs were low-pass filtered with the predetermined cut-off chosen with the same previously described filter. Spectral analysis consisted of applying the Fast Fourier Transform (FFT) on the AEGs to identify the highest peak power (DF) within the physiological AF range (4 to 12 Hz). The 3D DF maps are generated by colour-coding the DF values on the 3D LA shell. 3D phase maps were obtained by applying Hilbert Transform on the AEGs and calculating the inverse tangent of the ration of imaginary and real parts of the analytical signal and limited between -π (depolarization) and π (repolarization).The points of singularity (SPs) were defined as the points around which the phase rotates by 2π and were automatically identified on the phase maps. Up to three neighbouring points radii and π/4 maximum step between neighbours were considered.Results.Firstly, the number of SPs were reduced after PVI ablation showing the impact of substrate modification(15013vs 14391, -4%).The number of detected SPs was shown to change when 1 or 3 simultaneous neighbouring points radii are considered (baseline:15013vs 6910, -117%), with a similar trend also for the post-PVI(14391vs 5814, -148%).Moreover, the number of SP detected on the phase maps was shown to be method-dependent of filtering strategies(Figure 1), with fewer SPs identified as narrower the filter band applied: 1170±121(1-150Hz)vs 0.75±1.15(HDF) (baseline and radii 1); 1130±108(1-150 Hz) vs 0,25±0.50 (HDF) (post-PVI and radii 1); 557±106(1-150 Hz) vs 0±0 (HDF) (baseline and radii 3); 470±112 (1-150 Hz) vs 0±0 (HDF) (post-PVI and radii 3).Figure 1: The impact on the SP detection on the phase maps according to the radii (1 and 3) and the pre-processing strategy (1-150 Hz, 1-50 Hz, 1-15 Hz, 3-50 Hz, 3-15 Hz and HDF±1 Hz).Discussion and Conclusions.The work proposed here highlights that the characterisation of fibrillatory phase patterns is extremely method-dependent making it difficult to represent the true electrophysiology of fibrillation. Current commercial technologies generate 3D maps making use of signal processing techniques similar to the ones implemented here and are used to identify and target areas on the atrium responsible for AF maintenance. This may be one of the factors that contribute to the controversies on the identification of the arrhythmic event maintenance mechanisms observed when comparing commercial systems.</p

A K-Nearest Neighbour Classifier for Predicting Catheter Ablation Responses Using Noncontact Electrograms During Persistent Atrial Fibrillation

The mechanisms for the initiation and maintenance of atrial fibrillation (AF) are still poorly understood. Identification of atrial sites which are effective ablation targets remains challenging. Supervised machine learning has emerged as an effective tool for handling classification problems with multiple features. The main goal of this work is to use learning algorithms in predicting the responses of ablating electrograms and their effect on terminating AF and the cycle length changes. A total of 3,206 electrograms (EGMs) from ten persistent AF (persAF) patients were used. 5-fold cross-validation was applied, in which 80 % of the data were used as training set and 20 % used as validation. Dominant frequency (DF) and organisation index (OI) were calculated from EGMs (264 seconds) for all patients and used as input features. A k-nearest neighbour (KNN) classifier was trained using ablation lesion data and deployed in additional 17,274 EGMs that were not ablated. The classification accuracy of 85.2 % was achieved for the KNN classifier. We have proposed a supervised learning algorithm using DF features, which has shown the ability of accurately performing EGM signal classification that could be potentially used to identify ablation targets and become a robust real-time patient diagnosis system