This application provides a method and apparatus of analyzing the ECG frequency parameters with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases, which relates to the interdisciplinary field of biomedical and science engineering. The method includes obtaining ECG signals from subjects through the designed electrodes; calculating ECG frequency domain parameters of the subjects based on the proposed power spectrum model and getting the analytical validation results after studying and verifying the parameters; generating indicators based on the analytical validation results, which could be potentially used as alternative indicators for STEMI diagnosis; and alerting when the indicators meet preset abnormal conditions. The present embodiment is a powerful tool to diagnose STEMI diseases faster and more effectively and helps patients receive timely assistance and treatment.

This application provides a method and apparatus of analyzing the ECG frequency parameters with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases, which relates to the interdisciplinary field of biomedical and science engineering. The method includes obtaining ECG signals from subjects through the designed electrodes; calculating ECG frequency domain parameters of the subjects based on the proposed power spectrum model and getting the analytical validation results after studying and verifying the parameters; generating indicators based on the analytical validation results, which could be potentially used as alternative indicators for STEMI diagnosis; and alerting when the indicators meet preset abnormal conditions. The present embodiment is a powerful tool to diagnose STEMI diseases faster and more effectively and helps patients receive timely assistance and treatment.

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using a substantial amount of ECG data. The systems receive ECG data from a sensing device positioned on a patient such as one or more ECG leads. The system may include an application that communicates with an ECG platform running on a server(s) that processes and analyzes the ECG data, e.g., using neural networks for delineation of the cardiac signal and classification of various abnormalities, conditions and/or descriptors. The processed ECG data is communicated from the server(s) for display in a user-friendly and interactive manner with enhanced accuracy.

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 device for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia is disclosed. The device may have both implanted and external components and can communicate with other user devices such as smartphones and smartwatches for monitoring and alerting in response to detected medically relevant events or states of a patient. The processor is configured to provide event detection based upon various criteria including what is found to be statistically abnormal for a patient or what has been defined by a doctor to be abnormal. A patient's cardiovascular condition can be tracked over time using histogram, trend, and summary information related to heart rate and/or cardiac features such as those measured from the S-T segment of heartbeats. Heartbeats that are elevated but which are below what is defined as high, are used to provide medically relevant detections.

A device for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia is disclosed. The device may have both implanted and external components and can communicate with other user devices such as smartphones and smartwatches for monitoring and alerting in response to detected medically relevant events or states of a patient. The processor is configured to provide event detection based upon various criteria including what is found to be statistically abnormal for a patient or what has been defined by a doctor to be abnormal. A patient's cardiovascular condition can be tracked over time using histogram, trend, and summary information related to heart rate and/or cardiac features such as those measured from the S-T segment of heartbeats. Heartbeats that are elevated but which are below what is defined as high, are used to provide medically relevant detections.

Electrical impulses are received from a beating heart. The electrical impulses are converted to an ECG waveform. The ECG waveform is converted to a frequency domain waveform, which, in turn, is separated into two or more different frequency domain waveforms, which, in turn, are converted into a plurality of time domain cardiac electrophysiological subwaveforms and discontinuity points between these subwaveforms. The plurality of subwaveforms and discontinuity points are compared to a database of subwaveforms and discontinuity points for normal and abnormal patients or to a set of rules developed from the database. A bundle branches (BB) to J-Point (BB-J) interval is identified from the plurality of subwaveforms and discontinuity points based on the comparison. The ECG waveform with the BB-J interval annotated is displayed.