Sorin Crm Sas

Institut National De La Sante Et De La Recherche Medicale

Université De Rennes 1

Ela Medical S.a.s.

Chu De Rennes

Universi Ié De Rennes 1

Inserm (Institut National De La Sante Et De La Recherche Medicale)

Université De Rennes I

Centre Hospitalier Universitaire Pontchaillou

Centre Hospitalier Universitaire De Pontchaillou

Guy Carrault

Alfredo Hernandez

Jean-Luc Bonnet

Hector Romero

Fabienne Porée

Virginie Le Rolle

Amar Kachenoura

Renzo Dal Molin

Anissa Bourguiba

Christine Henry

Thomas Herrmann

Mélanie Mullemeester

Patrick Poulain

Nadine Khodor

Patrick Pladys

Antoine Simon

Mathieu Kuchenbuch

Laurent Vandenbroucke

Alain Beuchee

Hector Manuel Romero Ugalde

The invention relates to an active implantable medical device comprising a processing unit able to be alternately operated during a predetermined period of activity and on standby during a standby period in a cyclical manner, and means for acquiring data relating to physiological and/or physical activity. The device also comprises means for calculating a frequency analysis of the data acquired, said calculating means being capable of successively perform part of the frequency analysis during periods of activity of the processing unit.

Active implantable medical device that can perform a frequential analysis

The invention relates to a non-invasive system for determining the maturation of a baby, which comprises a module for sampling a cardiac or electroencephalographic signal from a baby and advantageously performs a conversion of a plurality of temporal samples derived from the cardiac signal or from the electroencephalic signal into a visibility graph, then a determination of at least one index on the basis of this visibility graph, a comparison of at least one determined index with one or more statistical indices representative of the maturation of a plurality of babies and a visual representation of a distance between at least one determined index and the statistical indices.

System for evaluating the maturation of a premature baby

An active implantable medical device for neurostimulation therapy is disclosed. The device produces stimulation pulse sequences generated continuously in succession during activity periods separated by intermediate inactivity periods during which no stimulation is issued. An input signal, provided by a physiological sensor, representative of cardiac activity and/or of the patient's hemodynamic status is received by circuitry. The circuitry further provides for dynamic control of the neurostimulation therapy, wherein the length of activity periods is modulated based on the current value level of the control parameter compared to a threshold. The duration of the next period of inactivity is calculated by the circuitry at the end of each activity period to maintain a constant duty cycle ratio between periods of activity and periods of inactivity.

Active implantable medical device for nerve stimulation therapy with dynamic adjustment of stimulation periods

The invention relates to an active implantable medical device. The device includes a VNS pulse generator, an activity sensor for detecting a patient's current activity level, and a processor. The processor is configured to collect the electrical activity of the heart, such as a current intrinsic heart rate of the patient. The processor further calculates values of a reference heart rate based on the patient's current activity level. A first histogram is constructed from the reference heart rate values, and a second histogram is constructed from the intrinsic heart rate values. An index representative of the patient's condition is derived by comparing the first and second histograms.

Active implantable medical device for heart failure treatment by stimulation of the vagus nerve

One system includes a stimulation device such as a vagus nerve stimulation lead, and a controller for controlling the stimulation device according to a set of stimulation parameters. A memory of the stimulation device contains a state transition model, and for each state defines a set of stimulation parameters and at least one expected response during the application of stimulation with the parameters. A matrix determines the transition rules between states based on physiological levels measured versus target levels. A state transition control unit determines, in an organized timely method, possible transitions between states according to the rules on physiological levels obtained in response to the implementation of the stimulation parameters of the current state, and a transition from a current state to a new state causes a corresponding change in the parameter set used for stimulation.

System for stimulation therapy of the vagus nerve by implementation of a self-adaptive state transition model based on physical or physiological levels

System for stimulation therapy of the vagus nerve by implementation of a state transition model operating at multiple temporal or spatial resolutions

One system includes a stimulation device such as a vagus nerve stimulation lead, and a controller for controlling the stimulation device according to a set of stimulation parameters. A memory of the stimulation device contains a state transition model, and for each state defines a set of stimulation parameters and at least one expected response during the application of stimulation with the parameters. A matrix determines the transition rules between states based on physiological levels measured versus target levels. A state transition control unit determines, in an organized timely method, possible transitions between states according to rules on the physiological levels obtained in response to the implementation of the stimulation parameters of the current state, and a transition from a current state to a new state causes a corresponding change in the parameter set used for stimulation.

System for stimulation therapy of the vagus nerve by implementation of a state transition model

System for stimulation therapy of the vagus nerve by implementation of a state transition model with a learning phase

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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Cesson-Sévigné, France

Guy Carrault