Cardiac arrhythmia remains a major cause of morbidity and mortality worldwide, yet current diagnostic and research tools lack the spatial and temporal resolution necessary to fully characterize the underlying electrical mechanisms. This dissertation presents a suite of high density microelectrode array (MEA) technologies and CMOS integrated circuits and systems that enable high resolution electrophysiological investigation in both in vivo and in vitro settings. Three MEA systems are developed for cardiac interfaces, spanning the whole heart, through living myocardial slices, and down to single cell resolution, while integrating multichannel biopotential acquisition, impedance and capacitance spectroscopy, and electrical stimulation. Collectively, these MEA systems advance the capabilities of cardiac and neural electrophysiology by providing high density, multifunctional platforms suitable for diagnostics, drug screening, and disease modeling. ... Read more

INTRODUCTION: Effective treatment of atrial fibrillation (AF) is challenging due to its heterogeneous nature. Persistent AF is driven by electropathology, defined as complex electrical conduction disorders resulting from electrical and structural damage to atrial tissue. Intraoperative high-resolution epicardial mapping allows for detailed analysis of electrophysiological parameters to identify electropathology. However, a more accessible and realistic visualization tool is needed to enhance comprehension and educational effectiveness for a wide range of users, including patients, medical students, and surgeons. Therefore, this study aims to create a VR tool for visualising electrophysiological parameters to further improve understanding of AF.

METHODS: A VR tool has been developed to visualise voltage, local activation time (LAT), wave label, and conduction block (CB) data using colour- and scale-coded arrays on a 3D anatomical heart model. The tool allows user-interactive control over display settings, time, and speed, and includes informative elements for an understandable interface. Validation involved 19 participants, assessing intuitive interaction (Explore category), task-based interaction (Perform category), accurate interpretation of colour and scale coding along with anatomical awareness (Understand category), and user perception of the experience (Experience category). Each category results in a score between 0-1. Statistical Spearman Rank Correlation tests and U-tests are conducted to assess the cross-category, demographic, and self-assessment correlations.

RESULTS: Median scores for the validation tests were 0.63 for the Explore category, 0.98 for the Perform category, 0.85 for the Understand category, and 0.91 for the User Experience category. The majority of the difficulties were encountered during the control of speed and time, as well as during the interpretation of the CB map. Some participants also reported complications regarding the easiness of control. Other reported complications relate to visual clarity and ease of navigation. Statistical analysis showed a positive correlation between performance and understanding (p ≤ 0.001), along with improved understanding (p = 0.045) and anatomical awareness (p = 0.031) in participants familiar with intra-operative epicardial mapping compared to those without such experience.

CONCLUSION: The VR application offers the capability to visualise electrophysiological parameters derived from patient-specific AF mapping in an accessible, understandable, educationally effective, and engaging manner. Improvements are needed in spatial accuracy, visual clarity, and guided user flow. Nevertheless, the tool has demonstrated functionality and provided valuable insights for users with varying experience levels, highlighting its potential as an analytical and educational resource.
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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. ... Read more

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. ... Read more