Abstract: A medical system is provided. The medical system includes a medical device and a mobile computing device. The medical device includes at least one physiologic sensor configured to acquire physiological signals from a patient, at least one processor coupled to the at least one physiologic sensor, and at least one optical code encoded with encrypted data. The mobile computing device includes a camera and one or more processors coupled to the camera and configured to acquire one or more images of the at least one optical code, decode the one or more images of the at least one optical code to generate a copy of the encrypted data, decrypt the copy of the encrypted data to generate decrypted data, and process the decrypted data to establish an operable connection between the mobile computing device and the medical device.

Abstract: A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.

Abstract: An apparatus for analyzing a patient's cardiac activity to detect cardiac events includes a plurality of ECG electrodes configured to be attached to the patient. The plurality of ECG electrodes are configured to generate two or more ECG leads. The apparatus also includes a processor in communication with the plurality of ECG electrodes. The processor is configured to generate a control vectorcardiogram loop trajectory, receive an ECG signal, generate, based on the ECG signal, a plurality of ECG vectors, and generate, from the plurality of ECG vectors, a test vectorcardiogram loop trajectory. The processor is also configured to determine at least one T wave measure by comparing the test vectorcardiogram loop trajectory to the control vectorcardiogram loop trajectory, identify, based at least partially on the at least one T wave measure, a trajectory bifurcation, and determine, based on the trajectory bifurcation, an indicator of a cardiac event.

Abstract: A system for determining T wave bifurcation that includes three or more ECG leads configured to receive an ECG signal and a first computing device including a processor coupled to a memory, the processor and the memory configured to perform operations including: generating at least two orthogonal ECG vectors based on the ECG signal of a patient, processing the at least two orthogonal ECG vectors to determine a loop trajectory of at least a portion of the ECG signal, identifying a trajectory bifurcation by comparing the loop trajectory to a control loop trajectory for a plurality of cardiac cycles, and determining an indicator of a cardiac event based on the trajectory bifurcation.

Abstract: A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.

Abstract: A system for determining T wave bifurcation that includes three or more ECG leads configured to receive an ECG signal and a first computing device including a processor coupled to a memory, the processor and the memory configured to perform operations including: generating at least two orthogonal ECG vectors based on the ECG signal of a patient, processing the at least two orthogonal ECG vectors to determine a loop trajectory of at least a portion of the ECG signal, identifying a trajectory bifurcation by comparing the loop trajectory to a control loop trajectory for a plurality of cardiac cycles, and determining an indicator of a cardiac event based on the trajectory bifurcation.

Abstract: A system for determining T wave bifurcation that includes three or more ECG leads configured to receive an ECG signal and a first computing device including a processor coupled to a memory, the processor and the memory configured to perform operations including: generating at least two orthogonal ECG vectors based on the ECG signal of a patient, processing the at least two orthogonal ECG vectors to determine a loop trajectory of at least a portion of the ECG signal, identifying a trajectory bifurcation by comparing the loop trajectory to a control loop trajectory for a plurality of cardiac cycles, and determining an indicator of a cardiac event based on the trajectory bifurcation.

Abstract: A system for determining T wave bifurcation that includes three or more ECG leads configured to receive an ECG signal and a first computing device including a processor coupled to a memory, the processor and the memory configured to perform operations including: generating at least two orthogonal ECG vectors based on the ECG signal of a patient, processing the at least two orthogonal ECG vectors to determine a loop trajectory of at least a portion of the ECG signal, identifying a trajectory bifurcation by comparing the loop trajectory to a control loop trajectory for a plurality of cardiac cycles, and determining an indicator of a cardiac event based on the trajectory bifurcation.

Abstract: A system for determining T wave bifurcation that includes three or more ECG leads configured to receive an ECG signal and a first computing device including a processor coupled to a memory, the processor and the memory configured to perform operations including: generating at least two orthogonal ECG vectors based on the ECG signal of a patient, processing the at least two orthogonal ECG vectors to determine a loop trajectory of at least a portion of the ECG signal, identifying a trajectory bifurcation by comparing the loop trajectory to a control loop trajectory for a plurality of cardiac cycles, and determining an indicator of a cardiac event based on the trajectory bifurcation.

Abstract: A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.

Abstract: A system for determining T wave bifurcation that includes three or more ECG leads configured to receive an ECG signal and a first computing device including a processor coupled to a memory, the processor and the memory configured to perform operations including: generating at least two orthogonal ECG vectors based on the ECG signal of a patient, processing the at least two orthogonal ECG vectors to determine a loop trajectory of at least a portion of the ECG signal, identifying a trajectory bifurcation by comparing the loop trajectory to a control loop trajectory for a plurality of cardiac cycles, and determining an indicator of a cardiac event based on the trajectory bifurcation.

Abstract: Arrhythmia may impact the determination of physiological information from a physiological signal. A patient monitoring system may detect the presence of arrhythmia based on changes in the physiological signal. Derived value data sets may be extracted from the physiological signal and calculations performed to generate arrhythmia features. The arrhythmia features may be used to generate an arrhythmia indicator that may indicate the presence of arrhythmia in the physiological signal.

Abstract: Arrhythmia may impact the determination of physiological information from a physiological signal. A patient monitoring system may detect the presence of arrhythmia based on changes in the physiological signal. Derived value data sets may be extracted from the physiological signal and calculations performed to generate arrhythmia features. The arrhythmia features may be used to generate an arrhythmia indicator that may indicate the presence of arrhythmia in the physiological signal.

Abstract: Arrhythmia may impact the determination of physiological information from a physiological signal. A patient monitoring system may detect the presence of arrhythmia based on changes in the physiological signal. Derived value data sets may be extracted from the physiological signal and calculations performed to generate arrhythmia features. The arrhythmia features may be used to generate an arrhythmia indicator that may indicate the presence of arrhythmia in the physiological signal.

Abstract: Arrhythmia may impact the determination of physiological information from a physiological signal. A patient monitoring system may detect the presence of arrhythmia based on changes in the physiological signal. Derived value data sets may be extracted from the physiological signal and calculations performed to generate arrhythmia features. The arrhythmia features may be used to generate an arrhythmia indicator that may indicate the presence of arrhythmia in the physiological signal.