A61B5/35

Heart signal waveform processing system and method
11478199 · 2022-10-25 · ·

A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user obtained via a differential voltage potential measurement concerning the heart of the user; associating a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to the user.

Heart signal waveform processing system and method
11478199 · 2022-10-25 · ·

A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user obtained via a differential voltage potential measurement concerning the heart of the user; associating a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to the user.

SYSTEMS AND METHODS OF IDENTITY ANALYSIS OF ELECTROCARDIOGRAMS

A set of training electrocardiograms (ECGs) for each of a plurality of subjects is processed using a machine learning model to generate an output for each training ECG of each of the plurality of subjects. Training ECGs for each subject are labeled with an identity of the subject. A machine learning model is trained by comparing the output generated for each training ECG to a corresponding label of the training ECG to generate an identity model to identify ECGs of a first subject of the plurality of subjects. A first ECG is received from an ECG sensor and input to the identity model, which generates an output indicating whether the first ECG corresponds to the first subject. In response to the output indicating that the first ECG does not correspond to the first subject, a condition that the first subject has or may develop is determined based on the output.

SYSTEMS AND METHODS OF IDENTITY ANALYSIS OF ELECTROCARDIOGRAMS

A set of training electrocardiograms (ECGs) for each of a plurality of subjects is processed using a machine learning model to generate an output for each training ECG of each of the plurality of subjects. Training ECGs for each subject are labeled with an identity of the subject. A machine learning model is trained by comparing the output generated for each training ECG to a corresponding label of the training ECG to generate an identity model to identify ECGs of a first subject of the plurality of subjects. A first ECG is received from an ECG sensor and input to the identity model, which generates an output indicating whether the first ECG corresponds to the first subject. In response to the output indicating that the first ECG does not correspond to the first subject, a condition that the first subject has or may develop is determined based on the output.

HEART GRAPHIC DISPLAY SYSTEM
20230129648 · 2023-04-27 ·

A system is provided for displaying heart graphic information relating to sources and source locations of a heart disorder to assist in evaluation of the heart disorder. A heart graphic display system provides an intra-cardiogram similarity (“ICS”) graphic and a source location (“SL”) graphic. The ICS graphic includes a grid with the x-axis and y-axis representing patient cycles of a patient cardiogram with the intersections of the patient cycle identifiers indicating similarity between the patient cycles. The SL graphic provides a representation of a heart with source locations indicated. The source locations are identified based on similarity of a patient cycle to library cycles of a library cardiogram of a library of cardiograms.

HEART GRAPHIC DISPLAY SYSTEM
20230129648 · 2023-04-27 ·

A system is provided for displaying heart graphic information relating to sources and source locations of a heart disorder to assist in evaluation of the heart disorder. A heart graphic display system provides an intra-cardiogram similarity (“ICS”) graphic and a source location (“SL”) graphic. The ICS graphic includes a grid with the x-axis and y-axis representing patient cycles of a patient cardiogram with the intersections of the patient cycle identifiers indicating similarity between the patient cycles. The SL graphic provides a representation of a heart with source locations indicated. The source locations are identified based on similarity of a patient cycle to library cycles of a library cardiogram of a library of cardiograms.

Electrocardiogram apparatus and method

The disclosure relates to a device and method of obtaining an electrocardiogram for a subject. The method comprises receiving electrical signals from at least two head-mounted sensors; and analysing said electrical signals to resolve shape and timing information for each of the P-, Q-, R-, S-, and T-waves available for the subject over a number of cardiac cycles, to derive a composite electrocardiogram, ECG, in which the composite electrocardiogram is derived using signals only from said head-mounted sensors.

Electrocardiogram apparatus and method

The disclosure relates to a device and method of obtaining an electrocardiogram for a subject. The method comprises receiving electrical signals from at least two head-mounted sensors; and analysing said electrical signals to resolve shape and timing information for each of the P-, Q-, R-, S-, and T-waves available for the subject over a number of cardiac cycles, to derive a composite electrocardiogram, ECG, in which the composite electrocardiogram is derived using signals only from said head-mounted sensors.

Method and system to detect r-waves in cardiac arrhythmic patterns

Computer implemented methods and systems for detecting arrhythmias in cardiac activity are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a far field cardiac activity (CA) data set that includes far field CA signals for beats. The method applies a feature enhancement function to the CA signals to form an enhanced feature in the CA data set. The method calculates an adaptive sensitivity level and sensitivity limit based on the enhanced feature from one or more beats within the CA data set and automatically iteratively analyzes a beat segment of interest by comparing the beat segment of interest to the current sensitivity level to determine whether one or more R-waves are present within the beat segment of interest. The method repeats the iterative analyzing operation while progressively adjusting the current sensitivity level until i) the one or more R-waves are detected in the beat segment of interest and/or ii) the current sensitivity level reaches the sensitivity limit. The method detects an arrhythmia within the beat segment of interest based on a presence or absence of the one or more R-waves and records results of the detecting of the arrhythmia.

Method for analyzing arrhythmia in real time, electrocardiogram monitoring device and storage medium

A method for analyzing arrhythmia in real time and an electrocardiogram monitoring device is disclosed. The method for analyzing arrhythmia in real time includes: acquiring a QRS template set currently used for arrhythmia analysis; determining whether each QRS template in the QRS template set is reliable; displaying information of a QRS template when the QRS template set contains an unreliable QRS template; determining the QRS template set according to an operation performed by a user on the information of the QRS template; and acquiring real-time electrocardiogram data, performing arrhythmia analysis on the real-time electrocardiogram data by using a determined QRS template set, and outputting an arrhythmia analysis result of the real-time electrocardiogram data in real time. The method for analyzing arrhythmia in real time may achieve the correction of a template of a special waveform by means of editing a currently created QRS template. When the same type of waveform is analyzed again, a correct arrhythmia analysis result may be provided, which may improve the accuracy of arrhythmia analysis and improve the quality of electrocardiogram monitoring.