Provided is a method for displaying electrocardiogram (ECG) data for the detection of myocardial ischemia, wherein a map is configured in the form of concentric circles using ST segments obtained through ECG measurement such that the inner circle consists of a “depression” origin and the outer circle consists of an “elevation” origin so as to display by connecting graphs or dots according to the measured ECG values of limb leads (frontal plane leads; I to aVF) and chest leads (precordial plane leads; V1 to V6), and the intuitive confirmation of the presence of subendocardial ischemia or transmural injury is enabled through the map, thereby making it possible to quickly and accurately recognize the patient's conditions and inducing prompt treatment.

Provided is a method for displaying electrocardiogram (ECG) data for the detection of myocardial ischemia, wherein a map is configured in the form of concentric circles using ST segments obtained through ECG measurement such that the inner circle consists of a “depression” origin and the outer circle consists of an “elevation” origin so as to display by connecting graphs or dots according to the measured ECG values of limb leads (frontal plane leads; I to aVF) and chest leads (precordial plane leads; V1 to V6), and the intuitive confirmation of the presence of subendocardial ischemia or transmural injury is enabled through the map, thereby making it possible to quickly and accurately recognize the patient's conditions and inducing prompt treatment.

The various embodiments of the present invention provide a system and method for a fully mobile, non-invasive, continuous system for monitoring the cardiovascular and musculoskeletal health of an individual during exercise, and for administering a protocol for ischemic pre-conditioning. The system includes a wearable devices affixed on the user with a chest strap, coupled with an application running on a computing device (smartphone/smartwatch), which performs various computations on the wearable device, and allows the user to get real time alerts during exercise, by way of vibrations or audio messages or notifications on the gateway device, to guide them through a protocol for ischemic pre-conditioning.

The various embodiments of the present invention provide a system and method for a fully mobile, non-invasive, continuous system for monitoring the cardiovascular and musculoskeletal health of an individual during exercise, and for administering a protocol for ischemic pre-conditioning. The system includes a wearable devices affixed on the user with a chest strap, coupled with an application running on a computing device (smartphone/smartwatch), which performs various computations on the wearable device, and allows the user to get real time alerts during exercise, by way of vibrations or audio messages or notifications on the gateway device, to guide them through a protocol for ischemic pre-conditioning.

An electrocardiogram information dynamic monitoring method and dynamic monitoring system. The method includes a dynamic monitoring device receiving monitoring reference data input by a user or issued by a server; the data collection on a tested object so as to obtain electrocardiogram data of the tested object; the characteristic identification on the electrocardiogram data so as to obtain characteristic signals of the electrocardiogram data, implementing cardiac activity classification on the electrocardiogram data according to the characteristic signals, obtaining cardiac activity classification information according to electrocardiogram basic rule reference data, and generating electrocardiogram event data, wherein the electrocardiogram event data comprises device ID information of the dynamic monitoring device; the dynamic monitoring device determining corresponding electrocardiogram event information according to the electrocardiogram event data, and determining whether the electrocardiogram event information is electrocardiogram abnormality event information; and outputting alarm information when the electrocardiogram event information is electrocardiogram abnormality event information.

An electrocardiogram information dynamic monitoring method and dynamic monitoring system. The method includes a dynamic monitoring device receiving monitoring reference data input by a user or issued by a server; the data collection on a tested object so as to obtain electrocardiogram data of the tested object; the characteristic identification on the electrocardiogram data so as to obtain characteristic signals of the electrocardiogram data, implementing cardiac activity classification on the electrocardiogram data according to the characteristic signals, obtaining cardiac activity classification information according to electrocardiogram basic rule reference data, and generating electrocardiogram event data, wherein the electrocardiogram event data comprises device ID information of the dynamic monitoring device; the dynamic monitoring device determining corresponding electrocardiogram event information according to the electrocardiogram event data, and determining whether the electrocardiogram event information is electrocardiogram abnormality event information; and outputting alarm information when the electrocardiogram event information is electrocardiogram abnormality event information.

A system and method for modeling and visualizing ST segment morphology in an ECG. Many cardiac conditions show ST-elevation in ECG data and may be misdiagnosed as a consequence. The exemplary embodiments model a segment in the ECG with a curve and extract features from the curve to discriminate between the cardiac conditions, including STEMI.

A method for automatic determining of a state of a heart of a patient based on multiple-lead ECG information of the patient, the method includes (a) receiving the multiple-lead ECG information of the patient, by a computerized system; and (b) applying one or more machine learning processes on the multiple-lead ECG information of the patient to determine the state of a heart of the patient, wherein the state of the heart comprises at least one heart condition of multiple types of heart conditions. One, some or all of the one or more machine learning processes was trained using a dataset that comprises multiple computer generated images, the multiple computer generated images represent images acquired by an image acquisition process of ECG plots, wherein the ECG plots are generated based on digital multiple-lead ECG signals.

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.