Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Abstract: Disclosed systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives electrocardiogram data from the electrocardiogram sensor and applies a machine learning model to the received electrocardiogram data. The machine learning model has been trained based on previous electrocardiogram data of a plurality of subjects. The electrocardiogram data of the plurality of subjects have one or more associated analyte measurements. The processing device may determine an indication of a level of the analyte based on the electrocardiogram data.

Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Abstract: 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.

Abstract: Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.

Abstract: Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.

Abstract: Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.

Abstract: Disclosed systems and method receive electrocardiogram (EKG) data of a subject. The EKG data comprises a record of at least a full beat of the subject. The EKG data is input into a machine learning model to generate an output. The output can include a segmentation of the full beat or a measurement of a QT interval of the subject.

Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Abstract: Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.

Abstract: Disclosed systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives electrocardiogram data from the electrocardiogram sensor and applies a machine learning model to the received electrocardiogram data. The machine learning model has been trained based on previous electrocardiogram data of a plurality of subjects. The electrocardiogram data of the plurality of subjects have one or more associated analyte measurements. The processing device may determine an indication of a level of the analyte based on the electrocardiogram data.

Abstract: Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.

Abstract: Apparatuses and methods (including methods of using such apparatuses) for de-noising electrocardiograms (ECGs) by manually or automatically adjusting the amount of filtering of an ECG signal. For example, real-time ECG signals may be filtered by combining in a weighted fashion an unfiltered portion of an ECG (or a filtered portion of the same ECG) with the same portion of the ECG that has been filtered. The weighting may be adjusted manually and/or automatically. Also described herein are methods for real-time filtering of ECG signals using a combination of filtering techniques including filtering to correct baseline wander, Savitzky-Golay denoising, and threshold smoothing. Multiple filtering techniques may be combined in a weighed manner to provide signal de-noising.

Abstract: Apparatuses and methods (including methods of using such apparatuses) for de-noising electrocardiograms (ECGs) by manually or automatically adjusting the amount of filtering of an ECG signal. For example, real-time ECG signals may be filtered by combining in a weighted fashion an unfiltered portion of an ECG (or a filtered portion of the same ECG) with the same portion of the ECG that has been filtered. The weighting may be adjusted manually and/or automatically. Also described herein are methods for real-time filtering of ECG signals using a combination of filtering techniques including filtering to correct baseline wander, Savitzky-Golay denoising, and threshold smoothing. Multiple filtering techniques may be combined in a weighed manner to provide signal de-noising.