Introduction Atrial fibrillation (AF) is the most common age-related, progressive tachyarrhythmia in the USA and in European countries. AF is associated with an increased risk of stroke, heart failure, impaired cognitive function, and increased mortality. An obstacle for optimal diagnosis and treatment is the relatively unknown (electro-)pathophysiology of AF. In combination with intra-operative cardiac mapping, accurate analysis of the AF burden using post-operative continuous rhythm registrations might provide great insight into the underlying mechanisms of AF development. However, manual analysis of these continuous rhythm registrations is both time-consuming and subject to interpretation. Therefore, the aim of this study is to develop an automated AF detection algorithm for use in the research setting. Methods Using 6,400 manually annotated 30-seconds electrograms (ECGs) derived from the post-operative continuous rhythm registrations in the Erasmus Medical Center (Rotterdam), and 192 annotated records from standard MIT-BIH ECG databases, a classifier was developed with three output classes: AF, No AF, and Unusable (due to noise/artefacts). QRS-complexes were detected using a method based on the Pan-Tompkins algorithm. Subsequently, P- and T-waves were detected and features were extracted, which can be grouped into eight groups: RR-interval characteristics, peak-interval characteristics, amplitude characteristics, P-wave characteristics, T-wave characteristics, QRS-morphology characteristics, autocorrelation characteristics, and noise. Multiple classifiers were trained using a training set containing 4,800 post-operative ECGs and a hidden test set containing the remaining 1,600 post-operative ECGs. The optimal classifier in terms of accuracy was further optimized. Results Optimal classification was achieved using boosted decision trees. For the hidden test set, this resulted in an accuracy of 96.44% (95% CI: 95.41% - 97.24%) for detection of AF with a false negative rate of 2.8% (95% CI: 1.5% - 4.9%) and a false positive rate of 3.8% (95% CI: 2.9% - 5.1%). Of all 74 misclassifications, 36 (49%) were made in the group with irregular rhythms without AF. Classification was mainly based on the RR-interval characteristics. Conclusion An automated AF classifier based on post-operative continuous rhythm registrations for use in the research setting was proposed. Careful use of the classifier in combination with manual validation of detected AF segments makes the classifier suitable for supervised research purposes.

Atrial fibrillation (AF) is a frequently encountered cardiac arrhythmia characterized by rapid and irregular atrial activity, which increases the risk of strokes, heart failure and other heart-related complications. The mechanisms of AF are complicated. Although various mechanisms were proposed in previous research, the precise mechanisms of AF are not clear yet and the optimal therapy for AF patients are still under debated. A higher success rate of AF treatments requires a deeper understanding of the problem of AF and potentially a better screening of the patients. In order to study AF, instead of using human body surface ECGs, we use the epicardial electrograms (EGMs) obtained directly from the epicardial sites of the human atria during open heart surgery. This data is measured using a high-resolution mapping array and exhibits irregular properties during AF. Although different studies have analyzed electrograms in time and frequency domain, there remain many open questions that require alternative and novel tools to investigate AF. Experience in signal processing suggests that incorporating the spatial dimension into the time-frequency analysis on the multi-electrode electrograms may provide improved insights on the atrial activity. However, the electrophysiologcial models for describing spatial propagation are relatively complex and non-linear such that conventional signal processing methods are less suitable for a joint space, time, and frequency domain analysis. It is also difficult to use very detailed electrophysiologcial models to extract tissue parameters related to AF from the high-dimensional data. In this dissertation, we wish to propose a radically different approach to study and analyze the EGMs from a higher abstraction level and from different perspectives to get more understanding of the characteristics of AF. We also aim to develop a simplified electrophysiological model that can capture the spatial structure of the data and propose an efficient method to estimate the tissue parameters, which are helpful to analyze the electropathology of the tissue, e.g., cell activation time or conductivity. In the first part of this study, we put forward a graph-time spectral analysis framework to analyze EGMs during normal heart rhythm and AF with a higher-level model. To capture the frequency content along both time domain and graph domain, we propose the joint graph and short-time Fourier transform, which allows us to evaluate the temporal and spatial variation of EGMs and capture the interaction between space and time. The spectral analysis of the EGMs helps us to recognize atrial fibrillation impact on the atrial activity and identify the differences between the atrial activity and the ventricular activity. We find that the difference in graph smoothness between the atrial and ventricular activities enables us to better extract the atrial activity from the noisy measurements. The second part of this study is to find a simplified but accurate enough electrophysiological model for the high dimensional EGMs and to make more efficient use of the data to detect the arrythmogenic substrate that causes abnormalities in atrial tissue. In this dissertation, we develop the cross power spectral density matrix (CPSDM) model of the multi-electrode EGMs and make use of an effective method called confirmatory factor analysis (CFA) to jointly estimate the model parameters. The conductivity, the activation time, and the anisotropy ratio are useful parameters to determine abnormalities in cardiac tissue and are therefore the target parameters to be estimated. With the reasonable assumptions that the conductivity parameters and the anisotropy parameters are constant across different frequencies and heart beats, and the activation time of cells are constant across different frequencies, we propose simultaneous CFA (SCFA) to jointly estimate these parameters using multiple frequencies and multiple heart beats. The identifiability conditions which need to be satisfied in the CFA problem are used to find the relationship between the desired resolution and the required amount of data. Evaluations on the simulated data and the clinical data demonstrate that the proposed method can localize the conduction blocks in the tissue and reconstruct the clinical EGMs well using the estimated parameters. @en

Disturbances in the propagation of the electrical wavefront can cause cardiac arrhythmia’s that can, in extreme cases, lead to cardiac arrest. By recording the electrical signals of the heart, a better understanding can be gained of the mechanisms behind cardiac arrhythmias. A method that can be used to investigate the state of the heart is electroanatomic mapping (EAM), where the local electrical activity is related to a point within a 3D anatomical reconstruction of the heart. By investigating the electrical activity, areas of scarring can be identified. Currently, the electrical activity is recorded with a plaque multi-electrode array at the Erasmus MC. Local activation maps can be recorded during sinus rhythm (SR), and with the use of a reference electrode, total activation maps are generated. This can only be done during SR. During arrhythmia’s, this is no longer possible due to temporal variation in electrical activity. To investigate the activation maps during arrhythmia, an electrode array that can record the total activity of the ventricles was designed and developed in this thesis. By studying the literature and with the help of medical experts, a requirement list was established. Different concept ideas were compared using a Harris profile to select the best design for further development. From the Harris profile it was concluded that a 3D printed sock was the best design for the electrode array. An investigation of the effects of the pattern and thickness on the stiffness and fatigue life of the sock were performed with the help of tensile tests and fatigue tests. From the results of the tensile and fatigue tests, a decision for the design of the electrode array was made. A 3D printed sock with the optimized design and electrodes were used to make epicardial recordings on a porcine heart in an ex vivo heart perfusion setup. The electrical recordings were later analyzed and visualized on a 3D model of the heart. Even though the resolution of the electrode array was lower than that of the epicardial mappings performed at the Erasmus MC, the results obtained from the electrode array looked promising. This is the first study that has developed a 3D printed electrode array the can record the electrical activity of the ventricular surface from multiple sites simultaneously. However, before the electrode array can be used for elucidating the mysteries behind epicardial activation during arrhythmia’s, further development is needed.

Background: Unipolar electrograms (U-EGMs) contain additional information about interatrial activation and conduction in their morphology, which may aid towards improved diagnosis and staging of atrial fibrillation (AF).Objective: The primary objective is to investigate regional differences in electrogram area (EA) during SR and AF and to design a patient-specific EA fingerprint, to characterize the arrhythmogenic substrate in patients with mitral valve disease (MVD).Methods: Patients (N = 42) either with (‘AF group’, N = 23) or without a history of AF (‘No AF group’, N = 19), undergoing elective open heart surgery underwent high-resolution mapping of the right atrium (RA), left atrium (LA) and pulmonary veins (PV) including Bachmann’s bundle (BB). Spatial distributions of mean EA, variance and total EA were determined in SR and AF. Absolute EA values were correlated with amplitude, an established metric in substrate mapping.Results: A total of 3104460 EAs were analysed and compared between rhythms, regions and groups (Table 3). EA was larger in AF [SR: 54.97 (42.87), AF: 57.03 (51.02), p < 0.01], but smaller per region except the RA. In patients with AF, EA was significantly smaller across all atrial regions. During AF, amplitude showed moderate correlation with EA at best [no AF: r = 0.54 vs. AF: = 0.51].Conclusion: The EA feature, entailing the U-EGM amplitude, duration and overall morphology, is suitable in signal fingerprinting to characterize the arrhythmogenic substrate and contains additional information compared with amplitude alone. Further studies are required to fine-tune the EA and implement EA-based classification.

Atrial fibrillation (AF) is a common age-related cardiac arrhythmia. AF is characterized by rapid and irregular electrical activity of the heart leading to a higher risk of stroke and heart failure. During AF, the upper chambers of the heart, called atria, experience chaotic electrical wave propagation. However, despite the various mechanisms introduced in the literature, there is still an ongoing debate on a precise and consistent mechanism underlying the initiation and perpetuation of AF. Some studies show that AF is rooted in impaired electrical conduction and structural damage of atrial tissue, known as electropathology. Atrial electrograms (EGMs) recorded directly from heart’s surface, provide an important diagnostic tool to localize and quantify the degree of electropathology in the tissue. However, the analysis of the electrograms is currently constrained by the lack of suitable methods that can reveal the hidden electrophysiological parameters of the tissue. These parameters can be used as local indication of electropathology in the tissue. We believe that understanding AF and improving AF therapy starts with developing a proper forward model that is accurate enough (from a physiological point of view) and simultaneously simple enough to allow for subsequent parameter estimation. Therefore, the main focus of this thesis is on developing a simplified forward model that can efficiently explain the observed EGM based on AF relevant tissue parameters. An initial step before performing any analysis on the data is to remove noise and artefacts. All atrial electrogram recordings suffer from strong far-field ventricular activities (VA). Therefore, as the first step, we propose a new framework for removal of VA from atrial electrograms, which is based on interpolation and subtraction followed by low-rank and sparse matrix decomposition. The proposed framework is of low complexity, does not require high resolution multi-channel recordings, or a calibration step for each individual patient. In the next step, we develop a simplified electrogram model. We represent the model in a compact matrix form and show its linear dependence on the conductivity vector, enabling the estimation of this parameter from the recorded electrograms. The results show that despite the low resolution and all simplifying assumptions, the model can efficiently estimate the conductivity map and regenerate realistic electrograms, especially during sinus rhythm. In the next contribution of this dissertation, we propose a new approach for a better estimation of local activation times for atrial mapping by reducing the spatial blurring effect that is inherent to electrogram recordings using deconvolution. Employing sparsity based regularization and first-order time derivatives in formulating the deconvolution problem, improved performance of transmembrane current estimation is obtained. In the final part, we focus on translating our findings from research to clinical application. Therefore, we studied the effect of electrode size on electrogram properties including the length of the block line observed on the resulting activation map, percentage of observed low voltage areas, percentage of electrograms with low maximum steepness, and the number of deflections in the recorded electrograms. @en