  In this work, we study in detail the performance of Higher Order Dynamic Mode
Decomposition (HODMD) technique when applied to echocardiography images. HODMD
is a data-driven method generally used in fluid dynamics and in the analysis of
complex non-linear dynamical systems modeling several complex industrial
applications. In this paper we apply HODMD, for the first time to the authors
knowledge, for patterns recognition in echocardiography, specifically,
echocardiography data taken from several mice, either in healthy conditions or
afflicted by different cardiac diseases. We exploit the HODMD advantageous
properties in dynamics identification and noise cleaning to identify the
relevant frequencies and coherent patterns for each one of the diseases. The
echocardiography datasets consist of video loops taken with respect to a long
axis view (LAX) and a short axis view (SAX), where each video loop covers at
least three cardiac cycles, formed by (at most) 300 frames each (called
snapshots). The proposed algorithm, using only a maximum quantity of 200
snapshots, was able to capture two branches of frequencies, representing the
heart rate and respiratory rate. Additionally, the algorithm provided a number
of modes, which represent the dominant features and patterns in the different
echocardiography images, also related to the heart and the lung. Six datasets
were analyzed: one echocardiography taken from a healthy subject and five
different sets of echocardiography taken from subjects with either Diabetic
Cardiomyopathy, Obesity, SFSR4 Hypertrophy, TAC Hypertrophy or Myocardial
Infarction. The results show that HODMD is robust and a suitable tool to
identify characteristic patterns able to classify the different pathologies
studied.

Image and Video Processing;Medical Physics