Abstract: Embodiments of the present disclosure are directed to an electrocardiogram (ECG) monitoring device embedded into the construct of a controller (e.g., video game controller, steering wheel etc.) to monitor a user's heart health and diagnose various conditions (e.g., AFib, tachycardia, bradycardia, etc.). The controller may comprise a set of electrodes including electrodes to contact the user's hands and one or more electrodes on the back of the controller that can be used to contact the user's extremities as a 3rd contact point to provide additional leads for higher accuracy ECG sensing. The set of electrodes may be positioned at locations on the controller where the user's hands are relatively stable, thus minimizing motion artifacts caused by muscular movement (thereby allowing for signal stability and longevity).

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: Embodiments of the present disclosure relate to determining an optimal location on the body of a person where heart sounds may be optimally heard. The optimal location may be determined at a time prior to the attempted auscultation and ECG data corresponding to the optimal location may be stored in a memory of an auscultation device. Subsequently, when a e.g., physician wishes to listen to the heart sounds of the person, the physician may place the auscultation device at a first location on the patient. The auscultation device may periodically perform an ECG at a current location and use the ECG data at the current location and the ECG data at the optimal location to determine and provide guidance to the physician regarding a direction in which the auscultation device should be moved in order to reach the optimal location.

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 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: 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: A digital stethoscope includes a stethoscope housing defining a housing edge. The digital stethoscope also includes a surface region secured to the stethoscope housing at the housing edge, and a number of microphones. The digital stethoscope also includes a processing device disposed within the stethoscope housing and in communication with the microphones. The processing device receives the digital audio data from the microphones.

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 mobile ECG sensors, systems and methods. Some embodiments provide ECG sensors in a packaging-label form factor that allows a user to contact two electrically isolated electrodes on an exterior of a product packaging to measure heart electrical signals for a Lead I ECG.

Abstract: Described herein are software platforms (or “platforms”), systems, devices, and methods for providing effective healthcare to a patient while increasing the efficiency of the healthcare provider. A platform provides effective healthcare to a patient through, but not limited to, monitoring of the physical parameters and therapeutic objectives of the patient. In addition, a platform is further configured to provide a communication link between a patient and a healthcare provider. The platform may be implemented by a system comprising one or more computing devices that interact with or run one or more applications of the platform.

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: A mobile electrocardiogram (ECG) device is described, comprising an electrode assembly comprising electrodes, wherein the electrode assembly senses heart-related signals when in contact with a body of a user, and produces electrical signals representing the sensed heart-related signals. The mobile ECG device further comprises a converter assembly electrically connected to the electrode assembly, configured to convert the electrical signals to a modulated signal, wherein the modulated signal carries the electrical signals representing the sensed heart-related signals. The mobile ECG device further comprises a transmitter that transmits the modulated signal wirelessly to a computing device. 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.

Abstract: A dashboard centered around arrhythmia or atrial fibrillation tracking is provided. The dashboard includes a heart or cardiac health score that can be calculated in response to data from the user such as their ECG and other personal information and cardiac health influencing factors. The dashboard also provides to the user recommendations or goals, such as daily goals, for the user to meet and thereby improve their heart or cardiac health score. These goals and recommendations may be set by the user or a medical professional and routinely updated as his or her heart or cardiac health score improves or otherwise changes. The dashboard is generally displayed from an application provided on a smartphone or tablet computer of the user.

Abstract: A personal monitoring device has a sensor assembly configured to sense physiological signals upon contact with a user's skin. The sensor assembly produces electrical signals representing the sensed physiological signals. A converter assembly, integrated with, and electrically connected to the sensor assembly, converts the electrical signals generated by the sensor assembly to a frequency modulated physiological audio signal having a carrier frequency in the range of from about 6 kHz to about 20 kHz.

Abstract: Described herein are devices, systems, and methods for sensing and generating a one or more of the 12 leads of a standard ECG with a mobile computing device. In some embodiments, three electrodes are positioned on a mobile computing device so that an individual may comfortably contact all three sensors at once to generate a six lead ECG which may be displayed on a display of said mobile computing device. In some embodiments, analysis carried out further comprises determining a QRS axis and QRST angle.

Abstract: Devices, systems, and methods for measuring and monitoring biometric or physiological parameters in a user-friendly and convenient manner are disclosed. Relevant physiological parameters of the user may be measured as the user normally operates a computing device or other hand-operated or hand-held device. These parameters are measured using an accessory of the device such as a laptop case, a tablet computer case, a smartphone case, or a smart watch or smart armband. The accessory may include at least two or three electrodes for taking an electrocardiogram or other physiological parameters. The measured parameters are transmitted to the computing device. The computing device can be normally used while a physiological parameter monitoring and measurement application loaded onto the computing device operates in the background to receive the measured parameters.

Abstract: Software applications, devices, systems, and methods are provided for monitoring, recording, and tracking cardiac health related metrics using an associated computing device. The software applications may monitor, record, and track physiological data such as cardiac electrical activity, heart rate, and blood pressure. Data transmission between a patient and a healthcare provider may also be enabled via the software applications disclosed herein.

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.