Abstract:

Background: Intracardiac electrograms are an indispensable part during diagnosis of supraventriculararrhythmias, but atrial activity (AA) can be obscured by ventricular far-fields (VFF). Concepts based onstatistical independence like principal component analysis (PCA) cannot be applied for VFF removalduring atrial tachycardia with stable conduction.Methods: A database of realistic electrograms containing AAand VFF was generated. Both PCA and thenew technique periodic component analysis (πCA) were implemented, benchmarked, and applied toclinical data.Results: The concept of πCA was successfully verified to retain compromised AA morphology,showing high correlation (cc = 0.98 ± 0.01) for stable atrial cycle length (ACL). Performance ofPCA failed during temporal coupling (cc = 0.03 ± 0.08) but improved for increasing conductionvariability (cc = 0.77 ± 0.14). Stability of ACL was identified as a critical parameter for πCAapplication. Analysis of clinical data confirmed these findings.Conclusion: πCA is introduced as a powerful new technique for artifact removal in periodic signals.Its concept and performance were benchmarked against PCA using simulated data and demonstratedon measured electrograms.

Abstract:

Catheter ablation has emerged as an effective treatment strategy for atrial fibrillation (AF) in recent years. During AF, complex fractionated atrial electrograms (CFAE) can be recorded and are known to be a potential target for ablation. Automatic algorithms have been developed to simplify CFAE detection, but they are often based on a single descriptor or a set of descriptors in combination with sharp decision classifiers. However, these methods do not reflect the progressive transition between CFAE classes. The aim of this study was to develop an automatic classification algorithm, which combines the information of a complete set of descriptors and allows for progressive and transparent decisions. We designed a method to automatically analyze CFAE based on a set of descriptors representing various aspects, such as shape, amplitude and temporal characteristics. A fuzzy decision tree (FDT) was trained and evaluated on 429 predefined electrograms. CFAE were classified into four subgroups with a correct rate of 81+/-3%. Electrograms with continuous activity were detected with a correct rate of 100%. In addition, a percentage of certainty is given for each electrogram to enable a comprehensive and transparent decision. The proposed FDT is able to classify CFAE with respect to their progressive transition and may allow objective and reproducible CFAE interpretation for clinical use.

Abstract:

State of the art biomedical engineering allows for acquiring enormous amounts of intracardiac data to aid diagnosis and treatment of cardiac arrhythmias. Modern catheters, which are used to acquire electrical information from within the heart, are capable of recording up to 64 channels simultaneously. The software available for data analysis, however, does not provide adequate performance to neither analyze nor visualize the acquired information in an appropriate manner. We present a software package that fascilitates interdisciplinary collaborations between engineers and physicians to adress open questions about pathophysiological mechanisms using data from everyday electrophysiogical studies. Therefore, a package has been compiled that enables algorithm development using MATLAB and subsequent visualization using the VTK C++ class libraries. The resulting application KaPAVIE, which is presented in this paper, is designed to meet the requirements from the clinical side and has been successfully applied in the clinical environment.

Abstract:

By means of computer modeling general comprehension of electrophysiology (EP) of human atria can be improved and simulated patterns of ectopic foci, reentry and rotors can be created. On the other hand atrial electrograms are measured in the EP lab of many hospitals every day. In this contribution simulated and measured clinical signals are compared critically aiming at better understanding of atrial fibrillation and validation of computer modeling.

Abstract:

Automatic signal processing of intracardiac electrograms plays a decisive role in the diagnosis and treatment of supraventricular arrhythmias. During sustained atrial flutter, a repetitive signal is measured in the atrium. However, the ventricular far field may overlap with the atrial activity and compromises the automatic signal processing tools during the intervention. Recently, a new method based on periodic component analysis was proposed as an artifact removal technique. The method works satisfactorily with highly periodic atrial activities but fails to reconstruct not regularly repeating signals .In order to account for that case, we developed a new method based on orthogonal component analysis to reconstruct the corrupted atrial electrocardiograms obscured by ventricular far field. We tested the method on synthetic signals and proved it to be successful. The reconstructed signals were of higher quality and the computation time was drastically shorter than the already existing periodic component analysis. We conclude that the new method can be used in realistic scenarios in the future.

Abstract:

Diagnosis of atrial flutter caused by ablation of atrial fibrillation is complex due to ablation scars. This paper presents an approach to replicate the clinically measured flutter circuit in a dynamic computer model. In a first step, important anatomical features of the flutter circuit are extracted manually based on the clinical measurement. With the help of this information, the electrical excitation propagation is simulated on the atrial geometry using the fast marching method. The simulated flutter circuit is used to estimate the global and local conduction velocity by approximating it iteratively. The parameterized flutter simulation is supposed to support the physician during diagnosis and treatment of atrial flutter.

Abstract:

The success rate of the cardiac ablation procedure to cure atrial fibrillation is moderate and depends on the experience and expertise of the physicians. It could be increased by precisely locating arrhythmogenic substrates. The aim of this work is to present a simple and feasible method to analyze intraatrial electrograms to identify the arrhythmogenic substrate on the atrium, under sinus rhythm and pacing sequences. The change in the depolarization wavefront propagation, resulting from consecutive triggering at a point in the coronary sinus (CS), can be an indication of the arrhythmogenic substrate. The region specific study enables the localization of critical sites in the patient specific atrial anatomy. This could aid the physicians in ascertaining the efficacy of cardiac therapies. In this work the point- to-point analysis of the intraatrial electrograms was carried out.

Abstract:

Catheter ablation is the most widely used minimum invasive technique to cure atrial arrhythmias. However, the success rate of the treatment is still moderate and depends on the experience and expertise of the physicians. The aim of this work is to present a simple and feasible method to identify the arrhythmogenic areas on the atrium based on the duration of atrial activities in the intraatrial electrograms. Depolarization waves are created by giving pacing impulses from coronary sinus (CS). The duration of the activity triggered from sinus node (SN) and pacing sequences are analysed by calculating the duration of the activity to mark regions with long atrial activitywaves. The intraatrial electrograms have been analysed on the basis of temporal and spatial information. The region specific study may favour the localization of the critical sites in the patient specific atrial anatomy and aid the physician in ascertaining the efficacy of the cardiac therapies. The identification of suitable markers for critical patterns of the depolarization waves may be crucial to guide an effective ablation treatment. In this work a novel study for point-to-point analysis of the intraatrial electrograms was carried out.