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 designed to store cardiac electrogram data in the memory and analyze the data 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 invention relates to a device for processing intracardiac signals, which comprises a memory unit (4) arranged to receive electrocardiogram data and synchronised electrogram data, a detector (6) arranged to analyse the electrocardiogram data and to detect QRS wave instants therein, an analyser (8) arranged to perform a wavelet transform of the electrogram data, an extractor (10) arranged to collect coefficients from the wavelet transform, each associated with a QRS wave instant detected by the detector (6), and to store them in a buffer (14), and a composer (12) arranged to extract a QRS fingerprint signal from the buffer (14) and subtract it from the wavelet transform at the QRS wave instants, and to output denoised electrogram data by inverse wavelet transform of the resulting signal.

Abstract: The inventions described herein relate to systems and methods directed to data-driven, continuous, and adaptable learning approaches to analyzing atrial tachycardia (AT) in a human body. The systems and methods may create an AT profile that automatically evolves such that a subsequent change in the AT is more accurately recognized and categorized.

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.