A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user; comparing one or more portions of the single-lead heartbeat waveform to one or more ML-generated waveform features to associate a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to a recipient.

A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user; comparing one or more portions of the single-lead heartbeat waveform to one or more ML-generated waveform features to associate a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to a recipient.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

An emergency cardiac and electrocardiogram (ECG) electrode placement device with artificial intelligence is disclosed herein. The emergency cardiac and electrocardiogram (ECG) electrode placement device incorporates electrical conducting materials and elastic material into a pad that is applied to a chest wall of a patient, which places multiple electrodes in the appropriate anatomic locations on the patient to quickly obtain an ECG in a pre-hospital setting. The AI program continuously runs EKGs to continuously monitor a patient.

An emergency cardiac and electrocardiogram (ECG) electrode placement device with artificial intelligence is disclosed herein. The emergency cardiac and electrocardiogram (ECG) electrode placement device incorporates electrical conducting materials and elastic material into a pad that is applied to a chest wall of a patient, which places multiple electrodes in the appropriate anatomic locations on the patient to quickly obtain an ECG in a pre-hospital setting. The AI program continuously runs EKGs to continuously monitor a patient.

Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In some examples, captured data including detected events is analyzed to identify likely overdetection of cardiac events. In some illustrative examples, when overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data. Several examples emphasize the use of morphology analysis using correlation to static templates and/or inter-event correlation analysis.

A system and method for display of subcutaneous cardiac monitoring data are provided. Cutaneous action potentials of a patient and other sensed data associated with the patient are recorded as electrocardiogram (EGC) data over a set time period using a subcutaneous insertable cardiac monitor. A set of R-wave peaks is identified within the ECG data and an R-R interval plot is constructed. A difference between recording times of successive pairs of the R-wave peaks in the set is determined. A heart rate associated with each difference is also determined. The pairs of the R-wave peaks and associated heart rate are plotted as the R-R interval plot. A diagnosis of cardiac disorder is facilitated based on patterns of the plotted pairs of the R-wave peaks, the associated heart rates in the R-R interval plot, and background data based on the other sensed data.

Systems and methods for pacing cardiac conductive tissue are described. An embodiment of a medical system includes an electrostimulation circuit to generate His-bundle pacing (HBP) pulses to stimulate a His bundle, and a cardiac event detector to detect a His-bundle activity within a time window following an atrial activity. The cardiac event detector may use a cross-chamber blanking, or an adjustable His-bundle sensing threshold, to avoid or reduce over-sensing of far-field atrial activity and inappropriate inhibition of HBP therapy. The electrostimulation circuit may deliver HBP in the presence of the His-bundle activity. The system may further recognize the detected His-bundle activity as either a FFPW or a valid inhibitory event, and deliver or withhold HBP therapy based on the recognition of the His-bundle activity.

Systems and methods for pacing cardiac conductive tissue are described. An embodiment of a medical system includes an electrostimulation circuit to generate His-bundle pacing (HBP) pulses to stimulate a His bundle, and a cardiac event detector to detect a His-bundle activity within a time window following an atrial activity. The cardiac event detector may use a cross-chamber blanking, or an adjustable His-bundle sensing threshold, to avoid or reduce over-sensing of far-field atrial activity and inappropriate inhibition of HBP therapy. The electrostimulation circuit may deliver HBP in the presence of the His-bundle activity. The system may further recognize the detected His-bundle activity as either a FFPW or a valid inhibitory event, and deliver or withhold HBP therapy based on the recognition of the His-bundle activity.

A method of determining a fused sensor measurement is disclosed including: obtaining sensor measurements from sensors detecting a same type of physiological measurement; determining a signal quality index (SQI) of each sensor including determining an extent to which a sensor measurement differs from others among the sensor measurements obtained from each sensor; determining a weightage of each sensor based on the SQI of each sensor; and determining a fused sensor measurement from the plurality of sensors based on the weightage of each sensor and filtered sensor measurements of each sensor obtained from a Kalman filter operation. A vehicle safety system includes: a vehicle electronic control unit configured to: determine the sensor measurement extent, to determine the SQI of each sensor, determine the weightage of each sensor, determine the fused sensor measurement, determine the occupant's physiological condition, and if the physiological condition is abnormal, perform at least one vehicle operation.