Abstract: A computer device for real-time analysis of electrograms, comprising a memory arranged to receive real-time electrograms signals each originating from one of a plurality of electrodes, a first evaluator comprising an extractor and a gradient boosting based machine learning module, said extractor being arranged to extract a set of features comprising at least one timewise analysis feature and at least one morphological feature from each electrogram signal within a set of electrogram signals, and to feed the resulting sets of features to said gradient boosting based machine learning module trained on data comprising sets of features labelled with a value indicating whether the associated electrogram signal exhibits dispersion and arranged to output for each set of electrogram.

Abstract: A device for detecting heart rhythm disorders comprises a memory designed to receive cardiac electrogram data and to store data defining a first classification model for detecting heart rhythm disorders and a second classification model for detecting heart rhythm disorders, a classifier designed to analyze cardiac electrogram data based on a classification model, and to return a classification value, and a driver design to store cardiac electrogram data in the memory and analyze them with the classifier, and to return alert data when the analysis by the classifier returns a classification value associated with a heart rhythm disorder.

Abstract: The present invention concerns a method for identifying areas of the heart of a patient able to be involved in the perpetuation of atrial fibrillation. This method takes into account the reference cycle of the arrhythmia and has two variants: a local variant in which the areas of the heart are each analysed separately and a regional variant in which several areas of the heart are analysed together. The invention also concerns device for implementing said method a program and the medium thereof.