Sorin Crm Sas

Ela Medical S.a.s.

Institut National De La Sante Et De La Recherche Medicale

Université De Rennes I

Centre Hospitalier Universitaire Pontchaillou

Fabienne Porée

Alfredo Hernandez

Guy Carrault

Amar Kachenoura

Renzo Dal Molin

Anissa Bourguiba

Antoine Simon

Patrick Pladys

The present invention relates to an active medical device that uses non-linear filtering for reconstructing a surface electrocardiogram from an endocardial electrogram. At least one endocardial EGM electrogram signal is collected from of samples collected from at least one endocardial or epicardial derivation ('), and at least one of a reconstructed surface electrocardiogram (ECG) signal through the processing of collected EGM samples by a transfer function (TF) of a neural network (&#x2032;). The neural network (&#x2032;) is a time-delay-type network that simultaneously processes said at least one endocardial EGM electrogram signal, formed by a first sequence of collected samples, and at least one delayed version of this EGM signal, formed by a second sequence of collected samples distinct from the first sequence collected samples. The neural network (&#x2032;) provides said reconstructed ECG signal from the EGM signal and its delayed version.

Reconstruction of a surface electrocardiogram from an endocardial electrogram using non-linear filtering

The present invention relates to an active medical device that uses non-linear filtering for reconstructing a surface electrocardiogram from an endocardial electrogram. At least one endocardial EGM electrogram signal is collected from of samples collected from at least one endocardial or epicardial derivation (), and at least one of a reconstructed surface electrocardiogram (ECG) signal through the processing of collected EGM samples by a transfer function (TF) of a neural network (). The neural network () is a time-delay-type network that simultaneously processes said at least one endocardial EGM electrogram signal, formed by a first sequence of collected samples, and at least one delayed version of this EGM signal, formed by a second sequence of collected samples distinct from the first sequence collected samples. The neural network () provides said reconstructed ECG signal from the EGM signal and its delayed version.

The reconstruction of a surface electrocardiogram based upon an endocardial electrogram. This method includes: (a) acquisition () of a plurality of endocardial electrogram signals (EGM) through a plurality of endocardial leads defined based upon endocardial electrodes; (b) calculation (), by combining the endocardial electrogram (EGM) signals acquired at step (a), of the corresponding endocardial vectogram (VGM); (c) angular rescaling () of the orthonormalized mark of the endocardial vectogram (VGM) with that of the surface vectocardiogram (VCG); (d) estimation (), based upon the endocardial vectogram (VGM) calculated at step (b), of a reconstructed surface vectocardiogram (VCGreconstructed), and (e) calculation () of the surface electrocardiogram (ECG) corresponding to said reconstructed surface vectocardiogram (VCGreconstructed).

Reconstruction of a surface electrocardiogram based upon an endocardial electrogram

The reconstruction of a surface electrocardiogram based upon an endocardial electrogram. This method includes: (a) acquisition () of a plurality of endocardial electrogram signals (EGM) through a plurality of endocardial leads defined based upon endocardial electrodes; (b) calculation (), by combining the endocardial electrogram (EGM) signals acquired at step (a), of the corresponding endocardial vectogram (VGM); (c) angular resealing () of the orthonormalized mark of the endocardial vectogram (VGM) with that of the surface vectocardiogram (VCG); (d) estimation (), based upon the endocardial vectogram (VGM) calculated at step (b), of a reconstructed surface vectocardiogram (VCGreconstructed), and (e) calculation () of the surface electrocardiogram (ECG) corresponding to said reconstructed surface vectocardiogram (VCGreconstructed).

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Rennes, France

Fabienne Porée