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.

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.