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Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this study, we simulate the propagation of the action potential (AP) in tissue affected by fibrotic changes and hence detect sites that initiate re-entrant activation patterns. By separately considering multiple different stimulus regimes, we directly observe and quantify the sensitivity of re-entry formation to activation sequence in the fibrotic region. Then, by extracting the fibrotic structures around locations that both do and do not initiate re-entries, we use neural networks to determine to what extent re-entry initiation is predictable, and over what spatial scale conduction heterogeneities appear to act to produce this effect. We find that structural information within about 0.5 mm of a given point is sufficient to predict structures that initiate re-entry with more than 90% accuracy.

Snímání biologického signálu patří v dnešní době mezi základní diagnostické prostředky. Jejich monitorováním lze nejen diagnostikovat chronické patologické procesy, ale lze jimi také odhalit (a do jisté míry predikovat) akutní stavy, které by mohly ohrozit lidský život. Tato práce je zaměřena na snímání mozkové aktivity, jejímž vyhodnocením může být rozeznána např. epilepsie, migréna, nebo lze vytvořit neurofeedback. Její náplní je vytvoření miniaturizovaného měřícího zařízení s bezdrátovým přenosem pro měření EEG a naprogramovat uživatelské prostředí pro zobrazení načtených dat., Taking a biological signal is nowadays one of the basic diagnostic tools. The monitoring can not only diagnose chronic pathological processes, but they can also detect (and to some extent predict) acute conditions that could endanger human life. This work is focused on capturing brain activity, by evaluating them can be recognize e.g. epilepsy, migraine, or you can create neurofeedback. The aim of work is to create a miniaturized measuring device with wireless transmission for measuring EEG and programmed user interface for displaying the read data., Ve zpracování, Import 05/08/2014