  Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart
muscle disease that appears between the second and forth decade of a patient's
life, being responsible for 20% of sudden cardiac deaths before the age of 35.
The effective and punctual diagnosis of this disease based on
Electrocardiograms (ECGs) could have a vital role in reducing premature
cardiovascular mortality. In our analysis, we firstly outline the
digitalization process of paper - based ECG signals enhanced by a spatial
filter aiming to eliminate dark regions in the dataset's images that do not
correspond to ECG waveform, producing undesirable noise. Next, we propose the
utilization of a low - complexity convolutional neural network for the
detection of an arrhythmogenic heart disease, that has not been studied through
the usage of deep learning methodology to date, achieving high classification
accuracy, namely 99.98% training and 98.6% testing accuracy, on a disease the
major identification criterion of which are infinitesimal millivolt variations
in the ECG's morphology, in contrast with other arrhythmogenic abnormalities.
Finally, by performing spectral analysis we investigate significant
differentiations in the field of frequencies between normal ECGs and ECGs
corresponding to patients suffering from ARVC. In 16 out of the 18 frequencies
where we encounter statistically significant differentiations, the normal ECGs
are characterized by greater normalized amplitudes compared to the abnormal
ones. The overall research carried out in this article highlights the
importance of integrating mathematical methods into the examination and
effective diagnosis of various diseases, aiming to a substantial contribution
to their successful treatment.
