In one aspect, a multi modal body sensor monitoring and recording system includes a personal status monitor (PSM) that communicates user bio-sensor data to an SCP. The PSM includes a controller comprising a sensing face, an intermediary circuit, and a mounting face. The controller provides a sensor array of specified biosensors. The controller is mountable with an ECG patch. The PSM includes an ECG patch coupled with the controller. The controller is removably mounted via comprising a sensor patch comprising a flat piece of material with an array of sensors arranged on a sensing face of the sensor patch of the sensor patch that is designed with a receptacle to which the controller device is connected into the ECG patch. The ECG patch obtains an ECG data o the user that is passed to the controller. The controller electronically communicates the ECG data and the specified biosensor data to the PHI server. The PHI server queries one or more health provider records systems to obtain a set of electronic health records, of the user. The PHI server electronically communicates the set of electronic health records to a system control program (SCP) server. The SCP server uses the biosensor data collected by the PSM, along with the PHI from electronic health records, to construct a virtual model of an individual's quantifiable biological markers in real time.

In one aspect, a multi modal body sensor monitoring and recording system includes a personal status monitor (PSM) that communicates user bio-sensor data to an SCP. The PSM includes a controller comprising a sensing face, an intermediary circuit, and a mounting face. The controller provides a sensor array of specified biosensors. The controller is mountable with an ECG patch. The PSM includes an ECG patch coupled with the controller. The controller is removably mounted via comprising a sensor patch comprising a flat piece of material with an array of sensors arranged on a sensing face of the sensor patch of the sensor patch that is designed with a receptacle to which the controller device is connected into the ECG patch. The ECG patch obtains an ECG data o the user that is passed to the controller. The controller electronically communicates the ECG data and the specified biosensor data to the PHI server. The PHI server queries one or more health provider records systems to obtain a set of electronic health records, of the user. The PHI server electronically communicates the set of electronic health records to a system control program (SCP) server. The SCP server uses the biosensor data collected by the PSM, along with the PHI from electronic health records, to construct a virtual model of an individual's quantifiable biological markers in real time.

An example method includes establishing a communications link between an electrophysiology (EP) monitoring system and an implantable medical device (IMD). IMD electrical data is received at the monitoring system via the communications link. The IMD electrical data may be synchronized with EP measurement data to provide synchronized electrical data based on timing of a synchronization signal sensed by an IMD electrode and/or EP electrodes. The method also includes computing reconstructed electrical signals for locations on a surface of interest within the patient's body based on the synchronized electrical data and geometry data. The geometry data represents locations of the EP electrodes, a location of the IMD electrode within the patient's body and the surface of interest.

Embodiments of the present disclosure utilize DNNs for ECG interpretation, where original ECG waveforms are directly ingested by the DNNs using paired interpretation labels for training, without the need for explicatory feature extraction or rule-based criteria.

The exemplified technology facilitates de-noising of magnetic field-sensed signal data (e.g., of an electrophysiological event) using signal reconstruction processes that fuse the magnetic field-sensed signal data with another sensed signal data (e.g., voltage gradient signal data) captured simultaneously with the magnetic field-sensed signal data. To this end, the purely algorithmic processing technique beneficially facilitates removal and/or filtering of noise from a sensor lead of a noisy captured source and rebuilds the signal for that lead from information simultaneously obtained from other leads of a different source. In some embodiments, a data are fused via a sparse approximation operation that uses candidate terms based on Van der Pol differential equations.

The exemplified technology facilitates de-noising of magnetic field-sensed signal data (e.g., of an electrophysiological event) using signal reconstruction processes that fuse the magnetic field-sensed signal data with another sensed signal data (e.g., voltage gradient signal data) captured simultaneously with the magnetic field-sensed signal data. To this end, the purely algorithmic processing technique beneficially facilitates removal and/or filtering of noise from a sensor lead of a noisy captured source and rebuilds the signal for that lead from information simultaneously obtained from other leads of a different source. In some embodiments, a data are fused via a sparse approximation operation that uses candidate terms based on Van der Pol differential equations.

A method for reconstructing 12-lead standard electrocardiogram (ECG) system signals using an M lead system, the method comprising recording signals acquired by the 12-lead standard ECG system; recording signals acquired by the M-lead system; and using the recorded signals to train a machine learning model to produce the reconstructed 12-lead standard ECG system signals using the M-lead system.

A method for reconstructing 12-lead standard electrocardiogram (ECG) system signals using an M lead system, the method comprising recording signals acquired by the 12-lead standard ECG system; recording signals acquired by the M-lead system; and using the recorded signals to train a machine learning model to produce the reconstructed 12-lead standard ECG system signals using the M-lead system.

Method for generating an activation map indicative of a time propagation of an action potential wavefront in a heart of a patient, the method being executed by a control unit and comprising the steps of: acquiring measured electrocardiography, ECG, data of the patient; generating, based on white noise, at least one set of identification parameters, each set of identification parameters identifying respective random ECG data and a respective random activation map that is indicative of a respective time propagation of a random action potential wavefront in the heart of the patient; generating, based on each set of identification parameters, said respective random ECG data; comparing the random ECG data and the measured ECG data to determine if there is correspondence between them; and if there is correspondence between the random ECG data and the measured ECG data, generating the activation map based on the at least one random activation map determined based on the at least one set of identification parameters used to obtain the random ECG data in correspondence with the measured ECG data.

Method for generating an activation map indicative of a time propagation of an action potential wavefront in a heart of a patient, the method being executed by a control unit and comprising the steps of: acquiring measured electrocardiography, ECG, data of the patient; generating, based on white noise, at least one set of identification parameters, each set of identification parameters identifying respective random ECG data and a respective random activation map that is indicative of a respective time propagation of a random action potential wavefront in the heart of the patient; generating, based on each set of identification parameters, said respective random ECG data; comparing the random ECG data and the measured ECG data to determine if there is correspondence between them; and if there is correspondence between the random ECG data and the measured ECG data, generating the activation map based on the at least one random activation map determined based on the at least one set of identification parameters used to obtain the random ECG data in correspondence with the measured ECG data.