Abstract: A smart watch to detect a presence of an arrhythmia of a user is disclosed. The smart watch comprising: a processing device and a photoplethysmography (“PPG”) sensor operatively coupled to the processing device, an ECG sensor comprising two or more ECG electrodes and operatively coupled to the processing device, a display operatively coupled to the processing device, and a memory, operatively coupled to the processing device. The memory may have instructions stored thereon that, when executed by the processing device, cause the processing device to: receive PPG data from the PPG sensor, receive ECG data from the ECG sensor, detect, based on the PPG data and the ECG data simultaneously, the presence of an arrhythmia, and provide one or more recommendations to the user based on the PPG data and the ECG data.

Abstract: The embodiments presented herein provide a device comprising a sensor assembly configured to sense physiological signals corresponding to physiological parameters of a user when in contact with the user, and to produce electrical signals representing the sensed physiological signals. The device may also comprise a converter assembly that is electrically connected to the sensor assembly, and is configured to convert the electrical signals to a modulated signal. The device may further comprise a transmitter that transmits the modulated signal wirelessly to a computing device. The device may include a housing that contains the sensor assembly, the converter assembly, and the transmitter.

Abstract: A smart watch to detect a presence of an arrhythmia of a user is disclosed. The smart watch comprising: a processing device and a photoplethysmography (“PPG”) sensor operatively coupled to the processing device, an ECG sensor comprising two or more ECG electrodes and operatively coupled to the processing device, a display operatively coupled to the processing device, and a memory, operatively coupled to the processing device. The memory may have instructions stored thereon that, when executed by the processing device, cause the processing device to: receive PPG data from the PPG sensor, receive ECG data from the ECG sensor, detect, based on the PPG data and the ECG data simultaneously, the presence of an arrhythmia, and provide one or more recommendations to the user based on the PPG data and the ECG 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 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 mobile electrocardiogram (ECG) device is described, comprising an electrode assembly comprising electrodes that sense heart-related signals when in contact with a body of a user, and produce electrical signals representing the sensed heart-related signals. The mobile ECG device further comprises a light-emitting device to facilitate an optimal placement of the electrode on the body of the user. The mobile ECG device further comprises a housing containing the electrode assembly, the converter assembly, the transmitter, and the light-emitting device. A first electrode of the electrode assembly forms a first side of the housing and comprises an opening through which the light-emitting device provides the UV light.

Abstract: Apparatuses and methods for extracting, de-noising, and analyzing electrocardiogram signals. Any of the apparatuses described herein may be implemented as a (or as part of a) computerized system. For example, described herein are apparatuses and methods of using them or performing the methods, for extracting and/or de-noising ECG signals from a starting signal. Also described herein are apparatuses and methods for analyzing an ECG signal, for example, to generate one or more indicators or markers of cardiac fitness, including in particular indicators of atrial fibrillation. Described herein are apparatuses and method for determining if a patient is experiencing a cardiac event, such as an arrhythmia.

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: 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: Described herein are devices, systems, and methods for sensing, generating, and time aligning one or more of the 12 leads of a standard ECG. A set of electrodes may be used to perform an electrocardiogram (ECG) of a user to generate a set of limb leads including a first lead I. A Wilson's Central Terminal (WCT) value may be determined using a subset of the set of limb leads. In response to an electrode of the set of electrodes contacting the user's chest, a precordial lead may be generated based on electric potentials sensed by the electrode while contacting the user's chest and the WCT value. As the precordial lead is generated, a corresponding second lead I is simultaneously generated. The corresponding second lead I is used to time align the set of limb leads and the precordial lead.

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 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: Described herein are methods, systems, and software for monitoring blood pressure. In some embodiments, a using a mobile device is used. The blood pressure monitoring system utilizes heart rate measurements at two separate locations on the body to calculate a differential pulse arrival time which is used to estimate blood pressure. The heart rate measurements can be taken simultaneously, or they can be taken sequentially while simultaneously taking ECG measurement. If taken sequentially, the heart rate measurements are aligned with the ECG to determine the differential. Accurate, inexpensive, and discreet blood pressure monitoring is thus provided.

Abstract: Described herein are systems, devices, and methods for cardiac monitoring. A method may include comparing, using a machine learning (ML) algorithm, an activity level of a user with one or more heart rate parameters of the user to determine whether a discordance is present between the activity level and the one or more heart rate parameters. In response to detecting that a discordance is present, performing an electrocardiogram (ECG) of the user to obtain ECG data of the user. An ML algorithm may be trained to detect an arrythmia using a historical set of training data including the activity level, the one or more heart rate parameters and previously recorded heart rate parameters, and a historical set of ECG data including the ECG data and previously recorded ECG data.

Abstract: Described herein are methods, systems, and software for monitoring blood pressure. In some embodiments, a using a mobile device is used. The blood pressure monitoring system utilizes heart rate measurements at two separate locations on the body to calculate a differential pulse arrival time which is used to estimate blood pressure. The heart rate measurements can be taken simultaneously, or they can be taken sequentially while simultaneously taking ECG measurement. If taken sequentially, the heart rate measurements are aligned with the ECG to determine the differential. Accurate, inexpensive, and discreet blood pressure monitoring is thus provided.

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: Described herein are systems, devices, and methods for cardiac monitoring. In particular, the systems, devices, and methods described herein may be used to conveniently sense the presence of an intermittent arrhythmia in an individual. The systems, devices, and methods described herein may be further configured to sense an electrocardiogram.

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: Described herein is an apparatus for monitoring various physiological parameters of a user. The apparatus may include a housing with a small or reduced form factor near the size of a credit or debit card, so as to allow a user to obtain e.g., an electrocardiogram (ECG) measurement from various locations. The apparatus may include an electrode assembly comprising a set of electrodes disposed on the housing to perform an ECG by sensing an electrical signal corresponding to heart activity of a user when in contact with skin of the user and output the electrical signal. The apparatus may detect or generate six or more ECG leads.