  Medical diagnoses can shape and change the life of a person drastically.
Therefore, it is always best advised to collect as much evidence as possible to
be certain about the diagnosis. Unfortunately, in the case of the Brugada
Syndrome (BrS), a rare and inherited heart disease, only one diagnostic
criterion exists, namely, a typical pattern in the Electrocardiogram (ECG). In
the following treatise, we question whether the investigation of ECG strips by
the means of machine learning methods improves the detection of BrS positive
cases and hence, the diagnostic process. We propose a pipeline that reads in
scanned images of ECGs, and transforms the encaptured signals to digital
time-voltage data after several processing steps. Then, we present a long
short-term memory (LSTM) classifier that is built based on the previously
extracted data and that makes the diagnosis. The proposed pipeline
distinguishes between three major types of ECG images and recreates each
recorded lead signal. Features and quality are retained during the digitization
of the data, albeit some encountered issues are not fully removed (Part I).
Nevertheless, the results of the aforesaid program are suitable for further
investigation of the ECG by a computational method such as the proposed
classifier which proves the concept and could be the architectural basis for
future research (Part II). This thesis is divided into two parts as they are
part of the same process but conceptually different. It is hoped that this work
builds a new foundation for computational investigations in the case of the BrS
and its diagnosis.

Signal Processing;Artificial Intelligence;Machine Learning;Machine Learning