The present invention relates to a digital content-based method for providing therapeutics information, the method comprising: a first step of performing stimulation on a brain of a user to obtain fNIRS (functional near-infrared spectroscopy) data of the user; a second step of extracting a first brain activation area from a plurality of brain areas of the user using the obtained fNIRS data; a third step of determining a first brain state of the user, based on the first brain activation area; a fourth step of providing the user with a content determined corresponding to the first brain state determined in the third step under an XR (Extended Reality) environment; a fifth step in which the user performs a mission corresponding to the content; a sixth step of extracting a second brain activation area from the plurality of brain areas with reference to the fNIRS data of the user following performing the mission; and a seventh step of determining a second brain state of the user, based on the second brain activation area; an eighth step of determining information related to amelioration of the brain state of the user.

In a method for extracting physiological data of an object under examination from ECG signals as part of MR imaging, raw ECG data comprising ECG signals may be captured from at least three electrodes located at different positions on an object under examination. The raw ECG data may be processed, which may include performing a first filtering using a first filter configured to extract an electrocardiogram, performing a second filtering using a second filter configured to identify a heartbeat, performing a third filtering using a third filter configured to extract and/or represent a respiratory movement, and/or performing a fourth filtering using a fourth filter configured to identify breathing. The processed raw ECG data including physiological data of the object under examination may be provided as an output.

In a method for extracting physiological data of an object under examination from ECG signals as part of MR imaging, raw ECG data comprising ECG signals may be captured from at least three electrodes located at different positions on an object under examination. The raw ECG data may be processed, which may include performing a first filtering using a first filter configured to extract an electrocardiogram, performing a second filtering using a second filter configured to identify a heartbeat, performing a third filtering using a third filter configured to extract and/or represent a respiratory movement, and/or performing a fourth filtering using a fourth filter configured to identify breathing. The processed raw ECG data including physiological data of the object under examination may be provided as an output.

A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.

A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.

A method for predicting multi-type ECG heart rhythms based on graph convolution includes: acquiring 12-lead ECG signals from a body surface of a patient, and resampling an ECG signal of each lead to a same signal length; constructing a node mutual information pooling U-shaped graph convolution network, and extracting deep features of the ECG signals by using a feature extraction module; performing one-layer one-dimensional convolution on the deep features to obtain a graph feature matrix to be constructed; inputting the obtained undirected graph into a graph encoding module in the graph convolution network, quantitatively calculating node mutual information of the undirected graph by using the graph encoding module, and selecting a node subset with the maximum mutual information to decrease the number of nodes in the undirected graph for down-sampling; inputting the undirected graph with the decreased number of nodes into a graph decoding module.

A method for predicting multi-type ECG heart rhythms based on graph convolution includes: acquiring 12-lead ECG signals from a body surface of a patient, and resampling an ECG signal of each lead to a same signal length; constructing a node mutual information pooling U-shaped graph convolution network, and extracting deep features of the ECG signals by using a feature extraction module; performing one-layer one-dimensional convolution on the deep features to obtain a graph feature matrix to be constructed; inputting the obtained undirected graph into a graph encoding module in the graph convolution network, quantitatively calculating node mutual information of the undirected graph by using the graph encoding module, and selecting a node subset with the maximum mutual information to decrease the number of nodes in the undirected graph for down-sampling; inputting the undirected graph with the decreased number of nodes into a graph decoding module.

Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.

Embodiments of the present disclosure relate to determining an optimal location on the body of a person where heart sounds may be optimally heard. The optimal location may be determined at a time prior to the attempted auscultation and ECG data corresponding to the optimal location may be stored in a memory of an auscultation device. Subsequently, when a e.g., physician wishes to listen to the heart sounds of the person, the physician may place the auscultation device at a first location on the patient. The auscultation device may periodically perform an ECG at a current location and use the ECG data at the current location and the ECG data at the optimal location to determine and provide guidance to the physician regarding a direction in which the auscultation device should be moved in order to reach the optimal location.

Embodiments of the present disclosure relate to determining an optimal location on the body of a person where heart sounds may be optimally heard. The optimal location may be determined at a time prior to the attempted auscultation and ECG data corresponding to the optimal location may be stored in a memory of an auscultation device. Subsequently, when a e.g., physician wishes to listen to the heart sounds of the person, the physician may place the auscultation device at a first location on the patient. The auscultation device may periodically perform an ECG at a current location and use the ECG data at the current location and the ECG data at the optimal location to determine and provide guidance to the physician regarding a direction in which the auscultation device should be moved in order to reach the optimal location.