A diagnostic ECG system analyzes lead traces for evidence of ST elevation in the lead signals. The pattern of ST elevation in leads having predetermined vantage points to the electrical activity of the heart and, in some instances, the presence of ST depression in certain other leads, identifies a specific coronary artery or branch as the culprit coronary artery for an acute ischemic event. ECG measurements which are associated with the identity of specific arterial occlusion locations are calculated and used to form a classifier of the probability of occlusion at different locations. The location identified as having the highest probability is indicated to a user as the most likely occlusion location.

A system for interactive ECG monitoring is described. The system includes a data repository storing pre-processed ECG data. The pre-processed ECG data is associated with historical data, real-time data, or both derived from a plurality of ECG recorders. The pre-processed ECG data includes ECG measurements extracted or derived from raw ECG signals and annotations of cardiac events. Further, the system includes a multi-agent query processor to receive and process an input message related to health of a subject, retrieve relevant data elements from the pre-processed ECG data, raw ECG signals, or both based on the processed input message, compute metrics corresponding to the input message based on the retrieved data elements, and generate a response to the input message using an LLM or at least one agent to integrate retrieved data elements and computed metrics. The response is presented on a user interface to a healthcare provider.

A system for interactive ECG monitoring is described. The system includes a data repository storing pre-processed ECG data. The pre-processed ECG data is associated with historical data, real-time data, or both derived from a plurality of ECG recorders. The pre-processed ECG data includes ECG measurements extracted or derived from raw ECG signals and annotations of cardiac events. Further, the system includes a multi-agent query processor to receive and process an input message related to health of a subject, retrieve relevant data elements from the pre-processed ECG data, raw ECG signals, or both based on the processed input message, compute metrics corresponding to the input message based on the retrieved data elements, and generate a response to the input message using an LLM or at least one agent to integrate retrieved data elements and computed metrics. The response is presented on a user interface to a healthcare provider.

A system and method for determining an arrhythmia risk are provided and include memory to store specific executable instructions and a machine learning (ML) model trained to predict an arrhythmia with a characteristic of interest (COI) that exhibits a non-physiologic behavior. One or more processors are configured to execute the specific executable instructions to obtain CA signals collected by an implantable medical device (IMD), wherein the COI exhibits a physiologic behavior and apply the ML model to the CA signals to identify a risk factor that a patient will experience the arrhythmia at a future point in time even though the COI in the CA signals, exhibits a physiologic behavior.

A system and method for determining an arrhythmia risk are provided and include memory to store specific executable instructions and a machine learning (ML) model trained to predict an arrhythmia with a characteristic of interest (COI) that exhibits a non-physiologic behavior. One or more processors are configured to execute the specific executable instructions to obtain CA signals collected by an implantable medical device (IMD), wherein the COI exhibits a physiologic behavior and apply the ML model to the CA signals to identify a risk factor that a patient will experience the arrhythmia at a future point in time even though the COI in the CA signals, exhibits a physiologic behavior.

A system and method are provided for the detection of a heart-related condition by obtaining information in real-time when a condition is initially identified as potentially occurring. A physical exercise and recovery episode is initially detected from physiological signals sensed in a patient. Once detected, a HR-ST segment deviation hysteresis analysis is performed in an implantable medical device (IMD) from certain physiological signals over portions of the exercise and recovery episode to identify the probability that a certain condition is occurring. Once a desired level of probability that the heart-related condition has been detected exists, data utilized in the analysis can be transmitted remotely for clinical review and confirmation of the device's detection of the condition. The patient may be prompted to answer questions related to symptoms that patient is experiencing through an input device in order further confirm the probability that the condition is occurring in the patient.

The object of the invention is a method for determining electrical activity of cardiac muscle, characterised in that the resultant electric potential (V.sub.wyp) forming the QRS complex in the electrocardiogram obtained during the ECG test is decomposed into partial potentials corresponding to the depolarization of specific areas (i) of the left ventricular muscle (MS).

The object of the invention is a method for determining electrical activity of cardiac muscle, characterised in that the resultant electric potential (V.sub.wyp) forming the QRS complex in the electrocardiogram obtained during the ECG test is decomposed into partial potentials corresponding to the depolarization of specific areas (i) of the left ventricular muscle (MS).

Disclosed herein, in some aspects, are systems and methods for detecting, monitoring, and managing a cardiac health status for a subject using ECG data. In some embodiments, the system receives health parameter measurements from one or more devices that are then used by a cardiac health tool (CHT) to determine a cardiac health status. Exemplary health parameter measurements include electrocardiogram (ECG) data from an ECG device and/or weight (from a weight scale for example). As described herein, in some embodiments, determining the cardiac health status includes a) detecting a cardiac condition in the subject, b) predicting a risk of a subject developing a cardiac condition (cardiac condition risk), and/or c) temporal monitoring of a cardiac health status for a subject. In some embodiments, the cardiac health tool is configured to determine the efficacy of a treatment or therapy applied to reduce the severity and/or risk of a cardiac condition.

Disclosed herein, in some aspects, are systems and methods for detecting, monitoring, and managing a cardiac health status for a subject using ECG data. In some embodiments, the system receives health parameter measurements from one or more devices that are then used by a cardiac health tool (CHT) to determine a cardiac health status. Exemplary health parameter measurements include electrocardiogram (ECG) data from an ECG device and/or weight (from a weight scale for example). As described herein, in some embodiments, determining the cardiac health status includes a) detecting a cardiac condition in the subject, b) predicting a risk of a subject developing a cardiac condition (cardiac condition risk), and/or c) temporal monitoring of a cardiac health status for a subject. In some embodiments, the cardiac health tool is configured to determine the efficacy of a treatment or therapy applied to reduce the severity and/or risk of a cardiac condition.