ABC | Volume 114, Nº3, March 2020

Original Article Sousa et al. Software: analysis of strain curves Arq Bras Cardiol. 2020; 114(3):496-506 Figure 6 – Figure 7 – Q-Q plots. 7a – EchoPAC 7b – D_Station 7c – Difference (EchoPAC - D-Station) –10 –15 –20 –25 –5 EchoPAC Quantiles –10 –15 –20 –25 –5 D_Station Quantiles –1.5 –0.5 –1.0 0.0 0.5 1.0 1.5 Difference Quantiles (EchoPAC - D-Station) –2 –1 0 1 2 Theoretical Quantiles –2 –1 0 1 2 Theoretical Quantiles –2 –1 0 1 2 Theoretical Quantiles The interface of D-Station with Circadapt model combined with the MultiPatch module allows the formulation of hypotheses and comparison of signals between real patients, as previously performed. 12-14 This contributes with the teaching of the pathophysiology of cardiac strain, in addition to potentially reduces the time to select the variables of interest and spare resources in the development of animal models in some research scenarios. The machine learning technique may be configured to process a great number of signals, identify variables of interest by data mining, and enable the use of the points of the strain curve/SR as described by Tabassian et al. 15 This can lead to extraction of further relevant data obtained from the study on cardiac strain, potentiated by the machine learning techniques, mainly by the imminent arrival of the high frame rate speckle tracking. 20 502