  Heart diseases are the main international cause of human defunction.
According to the WHO, nearly 18 million people decease each year because of
heart diseases. Also considering the increase of medical data, much pressure is
put on the health industry to develop systems for early and accurate heart
disease recognition. In this work, an automatic cardiac pathology recognition
system based on a novel deep learning framework is proposed, which analyses in
real-time echocardiography video sequences. The system works in two stages. The
first one transforms the data included in a database of echocardiography
sequences into a machine-learning-compatible collection of annotated images
which can be used in the training stage of any kind of machine learning-based
framework, and more specifically with deep learning. This includes the use of
the Higher Order Dynamic Mode Decomposition (HODMD) algorithm, for the first
time to the authors' knowledge, for both data augmentation and feature
extraction in the medical field. The second stage is focused on building and
training a Vision Transformer (ViT), barely explored in the related literature.
The ViT is adapted for an effective training from scratch, even with small
datasets. The designed neural network analyses images from an echocardiography
sequence to predict the heart state. The results obtained show the superiority
of the proposed system and the efficacy of the HODMD algorithm, even
outperforming pretrained Convolutional Neural Networks (CNNs), which are so far
the method of choice in the literature.
