Alejandro Alcaine

Electrocardiogram derived respiration from QRS slopes: Evaluation with stress testing recordings

A method for respiratory rate estimation from electrocardiogram (ECG), based on variations in QRS complexes slopes, is evaluated over stress testing recordings. Besides the 12 standard, and the 3 vectorcardiogram (VCG), 2 additional leads derived from the VCG are analyzed. A total of 34 slope series were studied, 2 for each lead: slopes between the peaks of the Q and R waves, and between the peaks of the R and S waves. Respiratory rate is estimated by using a time-frequency based algorithm which can combine information from several derived respiration signals. Evaluation was performed over a database containing ECG and respiratory signals simultaneously recorded from 30 subjects spontaneously breathing during a stress test. Respiratory rate estimation is performed with information of 4 different combinations of QRS slope series. The best results in respiratory rate estimation error terms are -1.07 ± 8.86% (-11.47 ± 37.97 mHz). These results suggest that proposed methods based on QRS slopes are highly suitable for respiratory rate estimation from ECG signal, specially at very non-stationary and noise scenarios as stress test.

A Wavelet-Based Electrogram Onset Delineator for Automatic Ventricular Activation Mapping

Electroanatomical mapping (EAM) systems are commonly used in clinical practice for guiding catheter ablation treatments of common arrhythmias. In focal tachycardias, the ablation target is defined by locating the earliest activation area determined by the joint analysis of electrogram (EGM) signals at different sites. However, this is currently a manual time-consuming and experience-dependent task performed during the intervention and thus prone to stress-related errors. In this paper, we present an automatic delineation strategy that combines electrocardiogram (ECG) information with the wavelet decomposition of the EGM signal envelope to identify the onset of each EGM signal for activation mapping. Fourteen electroanatomical maps corresponding to ten patients suffering from non-tolerated premature ventricular contraction (PVC) beats and admitted for ablation procedure were used for evaluation. We compared the results obtained automatically with two types of manual annotations: one during the intervention by an expert technician (on-procedure) and other after the intervention (off-procedure), free from time and procedural constraints, by two other technicians. The automatic annotations show a significant correlation (0.95, p <; 0.01) with the evaluation reference (off-procedure annotation sets combination) and has an error of 2.1 ± 10.9 ms, around the order of magnitude of the on-procedure annotations error (-2.6 ± 6.8 ms). The results suggest that the proposed methodology could be incorporated into EAM systems to considerably reduce processing time during ablation interventions.

A Multi-Variate Predictability Framework to Assess Invasive Cardiac Activity and Interactions During Atrial Fibrillation

Objective: This study introduces a predictability framework based on the concept of Granger causality (GC), in order to analyze the activity and interactions between different intracardiac sites during atrial fibrillation (AF). Methods: GC-based interactions were studied using a three-electrode analysis scheme with multi-variate autoregressive models of the involved preprocessed intracardiac signals. The method was evaluated in different scenarios covering simulations of complex atrial activity as well as endocardial signals acquired from patients. Results: The results illustrate the ability of the method to determine atrial rhythm complexity and to track and map propagation during AF. Conclusion: The proposed framework provides information on the underlying activation and regularity, does not require activation detection or postprocessing algorithms and is applicable for the analysis of any multielectrode catheter. Significance: The proposed framework can potentially help to guide catheter ablation interventions of AF.

Spatiotemporal model-based estimation of high-density atrial fibrillation activation maps

Examination of activation maps using multi-electrode array (MEA) sensors can help to understand the mechanisms underlying atrial fibrillation (AF). Classically, creation of activation maps starts with detection of local activation times (LAT) based on recorded unipolar electrograms. LAT detection has a limited robustness and accuracy, and generally requires manual edition. In general, LAT detection ignores spatiotemporal information of activation embedded in the relation between electrode signals on the MEA mapping sensor. In this work, a unified approach to construct activation maps by simultaneous analysis of activation patterns from overlapping clusters of MEA electrodes is proposed. An activation model fits on the measured data by iterative optimization of the model parameters based on a cost function. The accuracy of the estimated activation maps was evaluated by comparison with audited maps created by expert electrophysiologists during sinus rhythm (SR) and AF. During SR recordings, 25 activation maps (3100 LATs) were automatically determined resulting in an average LAT estimation error of - 0.66 ? 2.00 ? ms and a correlation of ? s = 0.98 compared to the expert reference. During AF recordings (235 maps, 28226 LATs), the estimation error was - 0.83 ? 6.02 ? ms with only a slightly lower correlation ( ? s = 0.93 ). In conclusion, complex spatial activation patterns can be decomposed into local activation patterns derived from fitting an activation model, allowing the creation of smooth and comprehensive high-density activation maps.

Estimation of high-density activation maps during atrial fibrillation

The study of activation maps using multi-electrode arrays (MEA) can help to understand atrial fibrillation (AF) mechanisms. Activation mapping based on recorded unipolar electrograms (u-EGM) rely on the local activation time (LAT) detector, which has a limited robustness, accuracy, and generally requires manual post-edition. In general, LAT detection ignores spatiotemporal information about activation and conduction conveyed by the relation between signals of the MEA sensor. This work proposes an approach to construct activation maps by simultaneous analysis of u-EGMs from small clusters of MEA electrodes. The algorithm iteratively fits an activation pattern model to the acquired data. Accuracy was evaluated by comparing with audited maps created by expert electrophysiologists from a patient undergoing open-chest surgery during AF. The estimation error was -0.29 ± 6.01 ms (236 maps, 28369 LATs) with high correlation (ρ = 0.93). Therefore, activation maps can be decomposed into local activation patterns derived from fitting an activation model, resulting in smooth and comprehensive high-density activation maps.

Very low frequency modulation in QRS slopes and its relation with respiration and heart rate variability during hemodialysis

In this work, we study the very low frequency (VLF) modulation (range 0.01-0.03 Hz) in QRS slopes, heart rate variability (HRV) and ECG-derived respiration in hemodialysis patients. First, the relation between QRS slopes and HRV in the VLF band is measured using ordinary coherence. Then, partial coherence is used to measure the former relationship once the effect related to respiration is removed. Ordinary coherence values above a statistical threshold revealed linear relationship between VLF modulation in QRS slopes and HRV in about 10% of analyzed segments, with mean ± SD values of 0.79 ± 0.07 for upward slope and 0.77 ± 0.06 for downward slope. For these segments, partial coherence values drop below the threshold for 64% of the cases for upward slope and 76% for downward slope, suggesting that the origin of the VLF modulation in QRS slopes is mainly driven by respiration or linearly related to it. In the rest of the cases, partial coherence values dropped with respect to ordinary coherence from 0.89 to 0.77 for upward slope and from 0.86 to 0.75 for downward slope, suggesting that other ANS effects non-linearly related to respiration also contribute to the VLF modulation in QRS slopes.

Automatic activation mapping and origin identification of idiopathic outflow tract ventricular arrhythmias

PURPOSE Activation mapping is used to guide ablation of idiopathic outflow tract ventricular arrhythmias (OTVAs). Isochronal activation maps help to predict the site of origin (SOO): left vs right outflow tract (OT). We evaluate an algorithm for automatic activation mapping based on the onset of the bipolar electrogram (EGM) signal for predicting the SOO and the effective ablation site in OTVAs. METHODS Eighteen patients undergoing ablation due to idiopathic OTVAs were studied (12 with left ventricle OT origin). Right ventricle activation maps were obtained offline with an automatic algorithm and compared with manual annotation maps obtained during the intervention. Local activation time (LAT) accuracy was assessed, as well as the performance of the 10ms earliest activation site (EAS) isochronal area in predicting the SOO. RESULTS High correlation was observed between manual and automatic LATs (Spearmans: 0.86 and Lins: 0.85, both p<0.01). The EAS isochronal area were closely located in both map modalities (5.55 ± 3.56mm) and at a similar distance from the effective ablation site (0.15±2.08mm difference, p=0.859). The 10ms isochronal area longitudinal/perpendicular diameter ratio measured from automatic maps showed slightly superior SOO identification (67% sensitivity, 100% specificity) compared with manual maps (67% sensitivity, 83% specificity). CONCLUSIONS Automatic activation mapping based on the bipolar EGM onset allows fast, accurate and observer-independent identification of the SOO and characterization of the spreading of the activation wavefront in OTVAs.

Study of electrogram organization and synchronization in paroxysmal and persistent/permanent atrial fibrillation

In this study, we measure and compare organization and synchronization of electrograms (EGM) obtained from patients with different types of diagnosed atrial fibrillation (AF). The studied database is composed of 70 patients admitted for ablation procedure and previously classified as paroxysmal or persistent/permanent AF. From those patients we analyzed bipolar EGM recordings during AF which were acquired using a circular catheter placed in the pulmonary veins. Results, which are given next as median (IQR), suggest that shorter (145.9 (27) ms vs. 161.3 (33.6) ms, p <; 0.01) and less stable AF cycle length (32.1 (36) ms vs. 7.2 (32.7) ms, p <; 0.01) is associated with persistent/permanent AF. Moreover, a significant reduction of the synchronization (0.35 (0.44) vs. 0.47 (0.51), p = 0.03) and linear relation (0.27 (0.40) vs. 0.36 (0.47), p = 0.01) between EGMs from antipodal electrodes of the same catheter has been found in persistent/permanent AF patients.