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.
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 5min. CONCLUSION: KaPAVIE(2) 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.
Whole-chamber mapping using a 64-pole basket catheter (BC) has become a featured approach for the analysis of excitation patterns during atrial fibrillation. A flexible catheter design avoids perforation but may lead to spline bunching and influence coverage. We aim to quantify the catheter deformation and endocardial coverage in clinical situations and study the effect of catheter size and electrode arrangement using an in silico basket model. Atrial coverage and spline separation were evaluated quantitatively in an ensemble of clinical measurements. A computational model of the BC was implemented including an algorithm to adapt its shape to the atrial anatomy. Two clinically relevant mapping positions in each atrium were assessed in both clinical and simulated data. The simulation environment allowed varying both BC size and electrode arrangement. Results showed that interspline distances of more than 20 mm are common, leading to a coverage of less than 50% of the left atrial (LA) surface. In an ideal in silico scenario with variable catheter designs, a maximum coverage of 65% could be reached. As spline bunching and insufficient coverage can hardly be avoided, this has to be taken into account for interpretation of excitation patterns and development of new panoramic mapping techniques.
Objective: Atrial tachycardia (AT) still pose 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 global cycle length coverage (gCLC) 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 sub-areas with 20mm 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: gCLC values of 100% were indicative for atria harbouring a reentrant driver. lCLC could detect micro reentries within an area of 1.65±1.28cm2 in simulated data and differentiate them against focal sources. Mid-diastolic potentials, being potential targets for catheter ablation, were identified as the 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.
AIMS: To test the ability of four circulating biomarkers of fibrosis, and of low left atrial voltage, to predict recurrence of atrial fibrillation after catheter ablation. BACKGROUND: Circulating biomarkers potentially may be used to improve patient selection for atrial fibrillation ablation. Low voltage areas in the left atrium predict arrhythmia recurrence when mapped in sinus rhythm. This study tested type III procollagen N terminal peptide (PIIINP), galectin-3 (gal-3), fibroblast growth factor 23 (FGF-23), and type I collagen C terminal telopeptide (ICTP), and whether low voltage areas in the left atrium predicted atrial fibrillation recurrence, irrespective of the rhythm during mapping. METHODS: 92 atrial fibrillation ablation patients were studied. Biomarker levels in peripheral and intra-cardiac blood were measured with enzyme-linked immunosorbent assay. Low voltage (<0.5mV) was expressed as a proportion of the mapped left atrial surface area. Follow-up was one year. The primary endpoint was recurrence of arrhythmia. The secondary endpoint was a composite of recurrence despite two procedures, or after one procedure if no second procedure was undertaken. RESULTS: The biomarkers were not predictive of either endpoint. After multivariate Cox regression analysis, high proportion of low voltage area in the left atrium was found to predict the primary endpoint in sinus rhythm mapping (hazard ratio 4.323, 95% confidence interval 1.337-13.982, p = 0.014) and atrial fibrillation mapping (hazard ratio 5.195, 95% confidence interval 1.032-26.141, p = 0.046). This effect was also apparent for the secondary endpoint. CONCLUSION: The studied biomarkers do not predict arrhythmia recurrence after catheter ablation. Left atrial voltage is an independent predictor of recurrence, whether the left atrium is mapped in atrial fibrillation or sinus rhythm.
G. Lenis, N. Pilia, T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. P wave detection and delineation in the ECG based on the phase free stationary wavelet transform and using intracardiac atrial electrograms as reference. In Biomedizinische Technik. Biomedical Engineering, vol. 61(1) , pp. 37-56, 2016
Robust and exact automatic P wave detection and delineation in the electrocardiogram (ECG) is still an interesting but challenging research topic. The early prognosis of cardiac afflictions such as atrial fibrillation and the response of a patient to a given treatment is believed to improve if the P wave is carefully analyzed during sinus rhythm. Manual annotation of the signals is a tedious and subjective task. Its correctness depends on the experience of the annotator, quality of the signal, and ECG lead. In this work, we present a wavelet-based algorithm to detect and delineate P waves in individual ECG leads. We evaluated a large group of commonly used wavelets and frequency bands (wavelet levels) and introduced a special phase free wavelet transformation. The local extrema of the transformed signals are directly related to the delineating points of the P wave. First, the algorithm was studied using synthetic signals. Then, the optimal parameter configuration was found using intracardiac electrograms and surface ECGs measured simultaneously. The reverse biorthogonal wavelet 3.3 was found to be optimal for this application. In the end, the method was validated using the QT database from PhysioNet. We showed that the algorithm works more accurately and more robustly than other methods presented in literature. The validation study delivered an average delineation error of the P wave onset of -0.32+/-12.41 ms when compared to manual annotations. In conclusion, the algorithm is suitable for handling varying P wave shapes and low signal-to-noise ratios.
Radiofrequency ablation has become a first-line approach for curative therapy of many cardiac arrhythmias. Various existing catheter designs provide high spatial resolution to identify the best spot for performing ablation and to assess lesion formation. However, creation of transmural and nonconducting ablation lesions requires usage of catheters with larger electrodes and improved thermal conductivity, leading to reduced spatial sensitivity. As trade-off, an ablation catheter with integrated mini electrodes was introduced. The additional diagnostic benefit of this catheter is still not clear. In order to solve this issue, we implemented a computational setup with different ablation scenarios. Our in silico results show that peak-to-peak amplitudes of unipolar electrograms from mini electrodes are more suitable to differentiate ablated and nonablated tissue compared to electrograms from the distal ablation electrode. However, in orthogonal mapping position, no significant difference was observed between distal electrode and mini electrodes electrograms in the ablation scenarios. In conclusion, catheters with mini electrodes bring about additional benefit to distinguish ablated tissue from nonablated tissue in parallel position with high spatial resolution. It is feasible to detect conduction gaps in linear lesions with this catheter by evaluating electrogram data from mini electrodes.
The waveguide invariant in shallow water environments has been widely studied in the context of passive sonar. The invariant provides a relationship between the frequency content of a moving broadband source and the distance to the receiver, and this relationship is not strongly affected by small perturbations in environment parameters such as sound speed or bottom features. Recent experiments in shallow water suggest that a similar range-frequency structure manifested as striations in the spectrogram exists for active sonar, and this property has the potential to enhance the performance of target tracking algorithms. Nevertheless, field experiments with active sonar have not been conclusive on how the invariant is affected by the scattering kernel of the target and the sonar configuration (monostatic vs bistatic). The experimental work presented in this paper addresses those issues by showing the active invariance for known scatterers under controlled conditions of bathymetry, sound speed profile and high SNR. Quantification of the results is achieved by introducing an automatic image processing approach inspired on the Hough transform for extraction of the invariant from spectrograms. Normal mode simulations are shown to be in agreement with the experimental results.
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.
There is evidence that rotors could be drivers that maintain atrial fibrillation. Complex fractionated atrial electrograms have been located in rotor tip areas. However, the concept of electrogram fractionation, defined using time intervals, is still controversial as a tool for locating target sites for ablation. We hypothesize that the fractionation phenomenon is better described using non-linear dynamic measures, such as approximate entropy, and that this tool could be used for locating the rotor tip. The aim of this work has been to determine the relationship between approximate entropy and fractionated electrograms, and to develop a new tool for rotor mapping based on fractionation levels. Two episodes of chronic atrial fibrillation were simulated in a 3D human atrial model, in which rotors were observed. Dynamic approximate entropy maps were calculated using unipolar electrogram signals generated over the whole surface of the 3D atrial model. In addition, we optimized the approximate entropy calculation using two real multi-center databases of fractionated electrogram signals, labeled in 4 levels of fractionation. We found that the values of approximate entropy and the levels of fractionation are positively correlated. This allows the dynamic approximate entropy maps to localize the tips from stable and meandering rotors. Furthermore, we assessed the optimized approximate entropy using bipolar electrograms generated over a vicinity enclosing a rotor, achieving rotor detection. Our results suggest that high approximate entropy values are able to detect a high level of fractionation and to locate rotor tips in simulated atrial fibrillation episodes. We suggest that dynamic approximate entropy maps could become a tool for atrial fibrillation rotor mapping.
Background: During atrial fibrillation, heterogeneities and anisotropies result in a chaotic propagation of the depolarization wavefront. The electrophysiological parameter called conduction velocity (CV) influences the propagation pattern over the atrium. We present a method that determines the regional CV for deformed catheter shapes, which result due to the catheter movement and changing wall contact.Methods: The algorithm selects stable catheter positions, finds the local activation times (LAT), considers the wall contact and calculates all CV estimates within the area covered by the catheter. The method is evaluated with simulated data and then applied to four clinical data sets. Both sinus rhythm activity as well as depolarization wavefronts initiated by stimulation are analyzed. The regional CV is compared with the fractionation duration (FD) and peak-to-peak (P2P) voltages. A speed of 0.5 m/s was defined to create the simulated LAT.Results: After analyzing the simulated LAT with clinical catheter spatial coordinates, the median CV of 0.5 m/s with an interquartile range of 0.22 and exact CV direction vectors were obtained. For clinical cases, the CV magnitude range of 0.08 m/s to 1.0 m/s was obtained. The P2P amplitude of 0.7 mV to 3.7 mV and the mean FD from 40.79ms to 48.66ms was obtained. The correlation of 0.86 was observed between CV and P2P amplitude, and 0.62 between CV and FD.Conclusion: In this paper, a method is presented and validated which calculates the CV for the deformed catheter and changing wall contact. In an exemplary clinical data set correlation between regional CV with FD and the P2P voltage was observed.
Physics-based detection algorithms can improve discrimination of sonar targets from competing bottom reverberation, but are vulnerable to environmental uncertainties. Recent research in the underwater community has identified an environmentally robust time-frequency signature for improved target discrimination. Application of this invariant requires processing algorithms to identify striations in a spectrogram and to quantify the associated track certainty. In this paper, two robust invariant-based algorithms are presented and demonstrated with underwater data. The first algorithm uses a Kalman Filter to estimate the time-frequency striations in sonar spectrograms. The second computes a likeliness metric to measure discrimination between target and non-target detections.
Local activation time (LAT) maps help to understand the path of electrical excitation in cardiac arrhythmias. They can be generated automatically from intracardiac electrograms using various criteria provided by commercial electroanatomical mapping systems. This study compares existing criteria and a novel method based on the non-linear energy operator (NLEO) with respect to their precision and robustness.
Simultaneous biatrial electroanatomical mapping was performed in a 54 year old woman using two 64-electrode basket catheters. Local activation time (LAT) maps were extracted retrospectively for single atrial excitations during sinus rhythm using the non-linear energy operator (NLEO). Considering both ampltiude and frequency information, the NLEO has shown to be a reliable estimator for the LAT. This paper presents an approach for creating biatrial LAT maps using the NLEO for single atrial excitations. The varying propagation pattern of individual beats reveals the presence and location of supraventricular extrasystoles.
T. Oesterlein, T. Baas, H. Malberg, and O. Dössel. Multivariate AR model parameter estimation on time series extracted from the ECG of myocarditis patients. In Biomedizinische Technik / Biomedical Engineering (Proceedings BMT2011), vol. 56(1) , 2011
Biosignal analysis is aiming at analyzing physiological parameters for improved diagnosis and treatment. In this paper, the use of multivariate autoregressive models is proposed as a new method to analyze ECG data and to gain further information about the functionality of the heart. This application is demonstrated on myocarditis patients, where cure and diagnosis was observed. Timeseries of RR and QT intervals are analyzed by an autoregressive (AR) model, whose parameters are found dependent on the condition of the inflammation. The heart muscle inflammation is known as potentially lethal and an invasive biopsy still is the gold standard. Due to the variety of its symptoms, detailled non-invasive diagnosis is rather difficult and thus a highly challenging topic.
Catheter ablation has become a very efficient strategy to terminate sustained cardiac arrhythmias like atrial flutter (AFlut). Identification of the optimal ablation spot, however, often proves difficult when scar from previous ablations is present. Although the application of electro-anatomical mapping systems allows to record thousands of intracardiac electrograms (EGMs) from each atrium, state-of-the-art techniques provide limited options for automatic signal processing. Goal of the presented research was the development of an algorithm to detect EGMs that present double potentials (DPs), as these often indicate functional or anatomical lines of block for cardiac excitation. Using an annotated database, we developed several features based on the morphological descriptors of DPs. These were used to train a binary decision tree which was able to detect DPs with a correct rate of over 90%.
T. Oesterlein, G. Lenis, A. Luik, C. Schmitt, and O. Dössel. Optimized Approach for the Detection of Active Segments in Intracardiac Electrograms Measured during Atrial Flutter. In 42nd International Congress on Electrocardiology Conference Book of Abstracts, 2015
T. Oesterlein, G. Lenis, A. Luik, C. Schmitt, and O. Dössel. Periodic component analysis to eliminate ventricular far field artifacts in unipolar atrial electrograms of patients suffering from atrial flutter. In Biomedizinische Technik / Biomedical Engineering, vol. 59(s1) , pp. 14, 2014
T. Oesterlein, G. Lenis, A. Luik, B. Verma, C. Schmitt, and O. Dössel. Removing ventricular far field artifacts in intracardiac electrograms during stable atrial flutter using the periodic component analysis proof of concept study. In Proceedings 41th International Congress on Electrocardiology, pp. 49--52, 2014
Post-ablation atrial flutter(AF) is a frequently occurring arrhythmia after treatment for persistent atrial fibrillation. However, mapping the flutter circuit using intracardiac electrograms is often challenging due to low signal voltage and scar areas caused by prior substrate modification. In addition, signals are frequently compromised by ventricular far field (VFF) artifacts, which obscure atrial activity (AA). This work introduces a new approach for VFF removal, which is based on the Periodic Component Analysis (􏰋CA). It utilizes the stable temporal relationship between AA and VFF, which poses a problem for other techniques like Principal Component Analysis (PCA) when both components superpose. A benchmark using simulated electrograms demonstrated significantly better correlation for this case when comparing pure AA to the reconstructed data using 􏰋CA instead of PCA (0.98 vs. 0.90, p<0.001). Its benefit for diagnosis is demonstrated on clinical data.
T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Neue Möglichkeiten zur Diagnose von Arrhythmien durch Visualisierung der zeitlichen Dynamik von Elektrogrammen. In Deutsche Gesellschaft für Kardiologie 80. Jahrestagung Mannheim, vol. 103(Suppl 1) , pp. V167, 2014
S. Bauer, T. Oesterlein, J. Schmid, and O. Dössel. Interactive visualization of cardiac anatomy and atrial excitation for medical diagnosis and research. In Current Directions in Biomedical Engineering, vol. 1(1) , pp. 400-404, 2015
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.
Today, patients suffering from atrial arrhythmias like atrial flutter (AFlut) or atrial fibrillation (AFib) are examined in the EP-lab (electrophysiology lab) in order to understand and treat the disease. Multichannel catheters are advanced into the atria in order to measureelectric signals at manyintracardiacpositions simultaneously. Complementary to clinical learning,comprehension of the disease and therapeutic strategies can be improved with computer modeling of the heart. This way, hypotheses about initiation and perpetuation of the arrhythmia can be tested and ablation strategies can be assessed in-silico. Modeling and biosignal analysis can benefit from mutual fertilization. On the one hand, modeling can be improved and personalization can be achieved via high density mapping of the atria. On the other hand, new algorithms for the interpretation of multichannel electrograms can be developed and evaluated with synthetic signals from computer models of the atria. This article illustrates the synergetic potential by examples and highlights challenges to be addressed in the future.
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.
Computer simulations and imaging of human physiology and anatomy are effectively used for diagnostics and medical treatments and are thus a focus of scientific research. Suitable representation of data is a critical aspect to achieve best results. Therefore, we developed an interactive visualization scheme especially for the representation of cardiac arrhythmias based on a conventional mobile device and virtual reality (VR) goggles (Google Cardboard and Samsung Gear VR) in combination with a game engine. The aim of this paper is to raise awareness for this new technique, evaluate its potential and pro- pose a general workflow for such a visualization environment. The use of a conventional mobile device in combination with VR goggles creates a portable and low-cost system, equipped with enough processing power and pixel density for many types of applications. The user can interact with the data through head movement or a secondary controller. As current game engines support a wide range of additional input methods and controllers, the interaction method can be customized to fit the target audience. To evaluate this method, we conducted a survey with eight typical phenomena from the field of cardiac arrhythmias. The participants were asked to rate different performance aspects on a scale from one (very bad) to five (very good). All participants (N=27) rated the performance as fluent (median=5). Furthermore, most participants (70%) ranked the overall impression as very good (median=5). On the long run, the system can be used for education and presentations as well as improved planning and guidance of medical procedures.
The novel high-density mapping system RhythmiaTM Medical (Boston Scientific, Marlborough, USA) allows a fast and automatic acquisition of intracardiac electrograms (EGMs). For recording the ORION mini-basket catheter is used. Due to the small electrode surface, the spatial averaging is smaller than with other commonly used mapping catheters. This results in a higher quality of unipolar signals. However, these are still corrupted by noise such as high frequency interference. Within this project, methods were developed and benchmarked that can be applied to detect and remove these undesired components. An algorithm was implemented to detect and eliminate artificial peaks in the spectrum of the EGM. The filtered signals showed improved quality in time domain. The performance of the spectral peak detection resulted in a median sensitivity of 92.1% and in a median positive predictive value of 91.9%.
G. Lenis, A. Kramlich, T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Development and Benchmarking of Activity Detection Algorithms for Intracardiac Electrograms Measured During Atrial Flutter. In Workshop Biosignal 2016. Innovation bei der Erfassung und Analyse bioelektrischer und bimagnetischer Signale, pp. 5-8, 2016
G. Lenis, T. Oesterlein, and O. Dössel. Orthogonal component analysis to remove ventricular far field in non periodic sustained atrial flutter. In Computing in Cardiology, vol. 42, pp. 669-672, 2015
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.
Catheter ablation of atrial fibrillation (AF) is still challenging and the sustaining mechanisms are discussed controversially. Basket mapping has emerged to a promising technique to detect temporary events like focal impulses fast changing fibrillation waves or meandering rotors.The aim of this study was to evaluate the atrial coverage of the basket catheter with respect to the distance of the electrodes to the endocardial surface and inter spline separation.
The goal of this research was to classify cardiac excitation patterns during atrial fibrillation (AFib). For this purpose, virtual models of intracardiac mapping catheters were moved across in-silico cardiac tissue to extract local activation times (LATs) of each catheter electrode from simulated cardiac action potential (AP) signals. The resulting LAT patterns consisting of the LATs of all electrodes resemble patterns measured in clinical cases. The LATs represent the input information for features that were used to separate four different excitation patterns during AFib. Those four excitation patterns were plane wave, ectopic focus (spherical wave), rotor (spiral wave) and block. A feature selection algorithm was used to investigate the features concerning their power to classify the different simulated excitation patterns. The scores of the selected features were used to train and optimize a support vector machine (SVM). The optimized and cross-validated SVM was then used to classify the simulated cardiac excitation patterns. The achieved overall classification accuracy of this SVM model was 98.4 %.
There is still a need for research to understand the co- herences of the origin of arrhythmias such like rotors and possible ablation strategies. The aim of this work was the analysis of typical signal characteristics near a rotor cen- ter. Rotors were simulated on 2D patch geometry (100 mm x 100 mm) with spatial resolution of 0.1mm. Based on extracellular potentials, different features were evalu- ated: Local activation time, peak to peak amplitude, steep- est negative slope and approximate entropy were com- pared regarding their ability to indicate the rotor tip lo- cation. Furthermore, typical signal patterns of different mapping catheters centered at the rotor tip position were analyzed. The determined maximum distances between the focal point of phase singularities and determined centers by the peak to peak amplitudes were maximal 1.7 mm.
M. Rottmann, T. Oesterlein, and O. Dössel. Local activation time based estimation of the direction of propagation of plane wave and the corresponding conduction velocity in simulated electrograms. In Biomedizinische Technik / Biomedical Engineering, vol. 59(s1) , pp. 152-155, 2014
Direction of propagation (DOP) and conduction velocity (CV) of excitation waves are essential parameters to identify targets for catheter ablation of cardiac arrhythmias. Most approaches to determine the DOP and CV rely on manual anno- tation. Many, time-consuming measurements with mapping catheters are required. Aim of this work was to quantitatively extract the DOP and the CV of wavefronts from intracardiac electrograms with a single shot measurement. We used a simulation database of planar waves computed with a cellular automaton with different CVs between 500 mm/s and 1100 mm/s. By comparing the correct values of CV and DOP with the computed values from the developed algorithm the median CV- error was between 7 mm/s and 50 mm/s and the median DOP- error variated between 1\0 and 4\0.
Aiming for patient specific treatment of atrial fibrillation, cardiologists in the EP-lab (ElectroPhysiology-lab) intend to identify the pattern of depolarization waves in the atria by measuring endocardial electrograms with multichannel catheters. Hereby the pattern of plane waves, ectopic foci, lines of block, or rotors are of special interest. Data acquisition is performed with various multichannel catheters, and all four patterns leave different fingerprints in the electrograms. In this work we extract features from the activation sequence in the electrograms that can support the cardiologist to identify the underlying depolarization pattern. To this end computer simulations of fundamental depolarization scenarios were carried out and the corresponding activation patterns were analyzed.
F. Schenkel, T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Detection and classification of atrial excitation patterns in intracardiac electrograms with application on biatrial basket catheter measurements. In Biomedizinische Technik / Biomedical Engineering, vol. 59(s1) , pp. 166-169, 2014
Atrial fibrillation is the most frequent cardiac arrhythmia and often shows a progressive development. An important source of supraventricular extrasystoles triggering paroxysmal atrial fibrillation are the pulmonary veins. Electrograms recorded using an intracardiac catheter can help to improve the classification and quantification of the different atrial excitations. This study presents a framework to recognize and quantify different atrial excitation patterns and to merge them into groups using a clustering method on the basis of the local activation time. The resulting templates can be annotated by physicians and used as a training data set for a classifier to allocate following data. On the basis of the classification result statistics about the origin and occurrence rate of the different excitation patterns could be provided.
ntracardiac electrograms are essential for the diagnosis and treatment of various cardiac arrhythmias. To gain reliable information about structural alterations of un- derlying tissue, it is necessary to interpret these electro- grams correctly. Therefore it has to be understood how other parameters influence the signal. Realistic 3D geome- tries were created and simulated using the bidomain model. Based on these simulations, the influences of catheter orien- tation, tissue thickness and conduction velocity on the amplitudes of intracardiac electrograms were evaluated.
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.
Recent studies about the development of endocardial radiofrequency (RF) ablation lesions (ALs) tried to identify reliable electrogram (EGM) markers for assessment of lesion transmurality. Additional clinically relevant information for physicians can be provided by examining endocardial EGM parameters like signal morphology, amplitude or time points in the signal. We investigated EGM features of the pulmonary vein ostia before and after RF ablation for three point-shaped lesions. Using high-density (HD) mapping, local activation time (LAT) and voltage maps were created, which provided information about the RF ALs regarding the lesion size and showed activation time delay as well as low-voltage areas with bipolar peak-to-peak voltages smaller than 2mV. The time delay of the depolarization front comparing the activation times anterior and posterior to the RF AL was up to 51.5 ms. In a circular area with 5mm radius around an RF AL the mean peak-to-peak voltage decreased by 62-94% to about 0.12-0.44mV and the mean maximal absolute EGM derivative was reduced by 65-96 %. Comparing the results of this study with EGMs of similar clinical settings confirmed our expectations regarding the low-voltage areas caused by the ablation procedure. An improved understanding of the electrophysiological changes is of fundamental importance to provide more information for enhanced RF ablation assessment.
Acquiring adequate mapping data in patients with atrial fibrillation is still one of the main obstacles in the treatment of this atrial arrhythmia. Due to the lack of catheters with both a panoramic field of view and sufficient electrode density for simultaneous mapping, electrophysiologists are forced to fall back on sequential mapping techniques. But, because activation patterns change rapidly during atrial fibrillation, they cannot be mapped sequentially. We propose that mapping tissue properties which are time independent, in contrast, allows a sequential approach. Here, we use the shortest measured electrogram cycle length to estimate the effective refractory period of the underlying tissue in a simulation study. Atrial fibrillation was simulated in a spherical model of the left atrium comprised of regions with varied refractory period. We found that the minimal measured electrogram cycle length correlates with the effective refractory period of the underlying tissue if the regions with distinct refractory properties are large enough and if the absolute difference in effective refractory periods is sufficient. This approach is capable of identifying regions of lowered effective refractory period without the need for cardioversion. Those regions are likely to harbor drivers of atrial fibrillation, which emphasizes the necessity of their localization.
B. Verma, T. G. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Combined analysis of unipolar and bipolar electrograms for local activation time annotation near the stimulus site of paced rhythms.. In Dreilandertagung Swiss, Austrian, and German society of Biomedical Engineering, 2016
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.
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.
B. Verma, T. Oesterlein, A. Luik, C. Schmitt, and O. Dössel. Combined analysis of unipolar and bipolar electrograms for local activation time annotation near the stimulus site of paced rhythms. In Current Directions in Biomedical Engineering, vol. 2, 2016
T. Oesterlein. Multichannel Analysis of Intracardiac Electrograms. Supporting Diagnosis and Treatment of Cardiac Arrhythmias. Dissertation. 2016
Cardiologists diagnose and treat atrial tachycardias using electroanatomical mapping systems. These can be combined with multipolar catheters to record intracardiac electrograms. Within this thesis, various signal processing techniques were implemented and benchmarked to analyze electrograms. They support the physician in diagnosis and treatment of atrial flutter and atrial fibrillation. The developed methods were assessed using simulated data and demonstrated on clinical cases.
T. Oesterlein. Multivariate AR model parameter estimation on time series extracted from the ECG and BP of myocarditis patients. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Dissertation. 2011
Student Theses (1)
T. Oesterlein. Monocular steady-state visual evoked potential based brain-computer interfaces by utilizing frequency/phase relationships. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Diplomarbeit. 2012
A Brain-Computer Interface based on Steady-State Visual Evoked Potentials was set up. It used both frequency and phase coding. Stimulation was performed monocular to retain situational awareness. Different signal processing algorithms were compared to retrieve the user's intend, namely the DFT, LockIn Analyzer and Canonical Correlation Analysis. The comparison and performance analysis was done offline using data of 10 subjects. Afterwards, prove of concept was achieved using a 6 command BCI to control a model fork truck.