Cardiologs Technologies Sas

Koninklijke Philips Corporation N.v.

Cardiologs Technologies

Jia Li

Chiara Scabellone

Julien Fontanarava

Mathurin Massias

Thomas Bordier

Cyril Gaudefroy

Benjamin Barre

Christophe Gardella

Mathieu Sornay

Romain Pomier

Jeremy Rapin

Jérémy Rapin

Gregoire De Masse

Marie-Albane De Saint Victor

Karen Prior

Systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using a substantial amount of ECG data. The systems receive ECG data from a sensing device positioned on a patient such as one or more ECG leads. The system may include an application that communicates with an ECG platform running on a server(s) that processes and analyzes the ECG data, e.g., using neural networks for delineation of the cardiac signal and classification of various abnormalities, conditions and/or descriptors. The ECG platform may further process and analyze the ECG data using neural networks and/or algorithms for embedding and grouping. The processed ECG data is used to generate a graphic user interface that is communicated from the server(s) to a computer for display in a user-friendly and interactive manner with enhanced accuracy.

Electrocardiogram processing system for delineation and classification

The present invention relates to a computer-implemented method for electrocardiogram analysis, the method comprising the steps of receiving at least one ECG signal; analyzing the ECG signal to provide features and/or identify at least one episode and/or event, wherein an episode is a segment of the ECG signal defined by a starting time, a duration and a label obtained during the analysis of the ECG signal and an event is a strip of the ECG signal of predefined duration defined by a starting time and a label obtained during the analysis of the ECG signal; and displaying a multiple field display () which includes at least a main plot (), being a global view of a graphic representation of the ECG signal in a first time window; a local view of a graphic representation of the ECG signal in a second time window (), where the first time window comprises the second time window; an intermediate view of a graphic representation of the ECG signal in a third time window (), wherein the third time window comprises the second time window and has a duration comprised between the duration of the first time window and the duration of the second time window.

User interface for analysis of electrocardiograms

Systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using a substantial amount of ECG data. The systems receive ECG data from a sensing device positioned on a patient such as one or more ECG leads/electrodes that may be integrated in a smart device. The system may include an application that communicates with an ECG platform running on a server(s) that processes and analyzes the ECG data, e.g., using neural networks, to detect and/or predict various abnormalities, conditions and/or descriptors. The system may also determine a confidence score corresponding to the abnormalities, conditions and/or descriptors. The processed ECG data is used to generate a graphic user interface that is communicated from the server(s) to a computer for display in a user-friendly and interactive manner with enhanced accuracy.

Electrocardiogram processing system for detecting and/or predicting cardiac events

Systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using a substantial amount of ECG data. The systems receive ECG data from a sensing device positioned on a patient such as one or more ECG leads. The system may include an application that communicates with an ECG platform running on a server(s) that processes and analyzes the ECG data, e.g., using neural networks for delineation of the cardiac signal and classification of various abnormalities, conditions and/or descriptors. The processed ECG data is communicated from the server(s) for display in a user-friendly and interactive manner with enhanced accuracy.

Disclosed is a method for computerizing delineation and/or multi-label classification of an ECG signal, including: applying a neural network to the ECG, labelling the ECG, and optionally displaying the labels according to time with the ECG signal.

Automatic method to delineate or categorize an electrocardiogram

The disclosure relates to systems and methods of converting a representation of a physiological signal (e.g., a non-digitized version such as a printed curve) into a digitized representation of the physiological signal of a subject. For example, a printed electrocardiogram (ECG) may be digitized using the systems are methods provided herein. The method may include receiving a digitized image of a printed curve representing the physiological signal of the subject, and detecting at least one region of interest having a portion of the physiological signal. For each of the regions of interest, the method may include extracting coordinates representing the physiological signal and registering the extracted coordinates.

Systems and methods for digitizing electrocardiograms

A method for computerizing delineation and/or multi-label classification of an ECG signal, includes: applying a neural network to the ECG whereby labelling the ECG, and optionally displaying the labels according to time, optionally with the ECG signal.

Growing community of inventors

Paris, France

Jia Li