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Intracardiac electrogram recordings during atrial fibrillation (AFib) are characterized by irregular rhythms and complex morphologies. Hence, analysis in the time domain is a difficult task. The so called dominant frequency DF is a spectrum based approach that aims at finding the most relevant frequency in a signal providing information about the rate and dynamics of AFib. However, in recent years various studies reported controversial results regarding the clinical relevance of the DF. In this work, a definition of the DF at a fundamental scale is proposed as the rate at which action potentials are triggered in atrial cells. The most common method to estimate the DF in literature, labeled as DFSpec, is examined in comparison to the proposed definition. A signal processing study using synthetic signals verified that the DFSpec is stable for all changes in morphology of atrial activations. However, it is also demonstrated that the DFSpec becomes unstable for variations above 20% in the cycle length of a signal. Spectrum based DF estimation should be interpreted in a critical manner and is not advisable for study endpoints or clinical markers. more...

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. more...

Objective: Unipolar intracardiac electrograms (uEGMs) measured inside the atria during electro-anatomic mapping contain diagnostic information about cardiac excitation and tissue properties. The ventricular far field (VFF) caused by ventricular depolarization compromises these signals. Current signal processing techniques require several seconds of local uEGMs to remove the VFF component and thus prolong the clinical mapping procedure. We developed an approach to remove the VFF component using data obtained during initial anatomy acquisition. Methods: We developed two models which can approximate the spatio-temporal distribution of the VFF component based on acquired EGM data: Polynomial fit, and dipole fit. Both were benchmarked based on simulated cardiac excitation in two models of the human heart and applied to clinical data. Results: VFF data acquired in one atrium were used to estimate model parameters. Under realistic noise conditions, a dipole model approximated the VFF with a median deviation of 0.029 mV, yielding a median VFF attenuation of 142. In a different setup, only VFF data acquired at distances of more than 5 mm to the atrial endocardium were used to estimate the model parameters. The VFF component was then extrapolated for a layer of 5 mm thickness lining the endocardial tissue. A median deviation of 0.082 mV (median VFF attenuation of 49x) was achieved under realistic noise conditions. Conclusion: It is feasible to model the VFF component in a personalized way and effectively remove it from uEGMs. Significance: Application of our novel, simple and computationally inexpensive methods allows immediate diagnostic assessment of uEGM data without prolonging data acquisition. [ABSTRACT FROM AUTHOR] more...

Objective: Atrial tachycardia (AT) still poses a major challenge in catheter ablation. Although state-of-the-art electroanatomical mapping systems allow to acquire several thousand intracardiac electrograms (EGMs), algorithms for diagnostic analysis are mainly limited to the amplitude of the signal (voltage map) and the local activation time (LAT map). We applied spatio-temporal analysis of EGM activity to generate maps indicating reentries and diastolic potentials, thus identifying and localizing the driving mechanism of AT. Methods: First, the time course of active surface area ASA is determined during one basic cycle length (BCL). The chamber cycle length coverage cCLC reflects the relative duration within one BCL for which activity was present in each individual atrium. A local cycle length coverage lCLC is computed for circular subareas with 20 mm diameter. The simultaneous active surface area sASA is determined to indicate the spatial extent of depolarizing tissue. Results: Combined analysis of these spatial scales allowed to correctly identify and localize the driving mechanism: cCLC values of 100% were indicative for atria harbouring a reentrant driver. lCLC could detect micro reentries within an area of 1.65 $\pm$ 1.28 cm $^2$ in simulated data and differentiate them against focal sources. Middiastolic potentials, being potential targets for catheter ablation, were identified as areas showing confined activity based on sASA values. Conclusion: The concept of spatio-temporal activity analysis proved successful and correctly indicated the tachycardia mechanism in 20 simulated AT scenarios and three clinical data sets. Significance: Automatic interpretation of intracardiac mapping data could help to improve the treatment strategy in complex cases of AT. [ABSTRACT FROM AUTHOR] more...

Background and objective Progress in biomedical engineering has improved the hardware available for diagnosis and treatment of cardiac arrhythmias. But although huge amounts of intracardiac electrograms (EGMs) can be acquired during electrophysiological examinations, there is still a lack of software aiding diagnosis. The development of novel algorithms for the automated analysis of EGMs has proven difficult, due to the highly interdisciplinary nature of this task and hampered data access in clinical systems. Thus we developed a software platform, which allows rapid implementation of new algorithms, verification of their functionality and suitable visualization for discussion in the clinical environment. Methods A software for visualization was developed in Qt5 and C++ utilizing the class library of VTK. The algorithms for signal analysis were implemented in MATLAB. Clinical data for analysis was exported from electroanatomical mapping systems. Results The visualization software KaPAVIE (Karlsruhe Platform for Analysis and Visualization of Intracardiac Electrograms) was implemented and tested on several clinical datasets. Both common and novel algorithms were implemented which address important clinical questions in diagnosis of different arrhythmias. It proved useful in discussions with clinicians due to its interactive and user-friendly design. Time after export from the clinical mapping system to visualization is below 5 min. Conclusion KaPAVIE 2 2 See http://www.ibt.kit.edu/hardundsoftware.php . is a powerful platform for the development of novel algorithms in the clinical environment. Simultaneous and interactive visualization of measured EGM data and the results of analysis will aid diagnosis and help understanding the underlying mechanisms of complex arrhythmias like atrial fibrillation. [ABSTRACT FROM AUTHOR] more...

Background Intracardiac electrograms are an indispensable part during diagnosis of supraventricular arrhythmias, but atrial activity (AA) can be obscured by ventricular far-fields (VFF). Concepts based on statistical independence like principal component analysis (PCA) cannot be applied for VFF removal during atrial tachycardia with stable conduction. Methods A database of realistic electrograms containing AA and VFF was generated. Both PCA and the new technique periodic component analysis (πCA) were implemented, benchmarked, and applied to clinical 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 of PCA failed during temporal coupling (cc = 0.03 ± 0.08) but improved for increasing conduction variability (cc = 0.77 ± 0.14). Stability of ACL was identified as a critical parameter for πCA application. 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 demonstrated on measured electrograms. [ABSTRACT FROM AUTHOR] more...