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.

翻译：心脏病是全球范围内导致人类功能丧失的主要原因。据世界卫生组织统计，每年近1800万人因心脏病死亡。随着医疗数据的增长，医疗行业正面临开发早期精准心脏病识别系统的巨大压力。本文提出一种基于新型深度学习框架的自动心脏病理识别系统，可实时分析超声心动图视频序列。该系统分两阶段运行：第一阶段将超声心动图序列数据库中的数据转化为机器学习兼容的标注图像集合，这些图像可用于各类机器学习框架（尤其是深度学习）的训练阶段。该阶段首次（据作者所知）将高阶动态模态分解（HODMD）算法应用于医学领域的数据增强与特征提取。第二阶段重点构建和训练相关文献中鲜有探索的视觉Transformer（ViT），并针对小数据集进行从头训练的适配改造。所设计的神经网络通过分析超声心动图序列图像预测心脏状态。结果表明，本系统性能优越，HODMD算法效果显著，甚至优于目前文献中首选的预训练卷积神经网络（CNN）方法。