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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 tobe certain about the diagnosis. Unfortunately, in the case of the BrugadaSyndrome (BrS), a rare and inherited heart disease, only one diagnosticcriterion exists, namely, a typical pattern in the Electrocardiogram (ECG). Inthe following treatise, we question whether the investigation of ECG strips bythe means of machine learning methods improves the detection of BrS positivecases and hence, the diagnostic process. We propose a pipeline that reads inscanned images of ECGs, and transforms the encaptured signals to digitaltime-voltage data after several processing steps. Then, we present a longshort-term memory (LSTM) classifier that is built based on the previouslyextracted data and that makes the diagnosis. The proposed pipelinedistinguishes between three major types of ECG images and recreates eachrecorded lead signal. Features and quality are retained during the digitizationof the data, albeit some encountered issues are not fully removed (Part I).Nevertheless, the results of the aforesaid program are suitable for furtherinvestigation of the ECG by a computational method such as the proposedclassifier which proves the concept and could be the architectural basis forfuture research (Part II). This thesis is divided into two parts as they arepart of the same process but conceptually different. It is hoped that this workbuilds a new foundation for computational investigations in the case of the BrSand its diagnosis.

Teaching a Machine to Diagnose a Heart Disease; Beginning from digitizing scanned ECGs to detecting the Brugada Syndrome (BrS)