Artificial pacemakers  and implantable cardioverter-defibrillators (ICDs) provide life-sustaining therapies for those affected by cardiac diseases. With  the aim of supporting follow-up care modern implants transmit diagnostic data to the manufacturer. The data includes an electrocardiogram (ECG) and is made available to the attending physician. An additional monitoring by means of machine learning methods is largely unresearched but holds great potential considering the recent advances in medical data science.

In the first part of the thesis an infrastructure that enables research on the transmitted ECGs was developed. It allows the data to be accessed  language-independent and approximately 120 times faster compared to a formerly utilized approach. Secondly, two machine learning methods originally designed for surface ECGs were tested on intracardiac signals. The first classifier was based on manual feature engineering. The second employed a convolutional neural network (CNN). Both were used to distinguish between supraventricular tachycardia (SVT) and ventricular tachycardia (VT) addressing current issues in ICD therapy. Compared to conventional SVT/VT detection algorithms they received a significantly smaller part of the ECG. With an accuracy of 94.9% the CNN outperformed the manual feature engineering (86.0%) and equaled the performance of the classifier used to annotate the training data.

The tested classifiers indicate not only that research on surface ECGs is transferable to intracardiac signals but also showed the importance of the new infrastructure.