Closed-loop stimulation of the Vagus nerve in response to a detected myocardial ischemia state within a therapeutic window can mitigate or reverse effects of the ischemia. This window is between 0 and 50 seconds of the onset of ischemia, before the myocardial ischemia reaches a statistically significant evolution level. A properly trained machine learning system such as a long short-term memory system can be used to analyze cardiovascular features and detect myocardial ischemia within the therapeutic window.

Data is generated that describes features of an ECG of a subject. This generation can include receiving ECG data that was generated to reflect cardiac activity of a particular mammal; submitting the ECG data to a plurality of cardiac classifiers, each cardiac classifier configured to identify, in the ECG, at least some of a plurality of cardiac features that are within a particular feature-class; receiving, from each of the plurality of cardiac classifiers, a classification message containing data of the cardiac classifiers identifying of cardiac features in the ECG; and assembling, from the received classification messages, ECG features for the ECG, the ECG features identifying at least some features of different feature-classes.

Data is generated that describes features of an ECG of a subject. This generation can include receiving ECG data that was generated to reflect cardiac activity of a particular mammal; submitting the ECG data to a plurality of cardiac classifiers, each cardiac classifier configured to identify, in the ECG, at least some of a plurality of cardiac features that are within a particular feature-class; receiving, from each of the plurality of cardiac classifiers, a classification message containing data of the cardiac classifiers identifying of cardiac features in the ECG; and assembling, from the received classification messages, ECG features for the ECG, the ECG features identifying at least some features of different feature-classes.

An electrocardiogram information processing method and workstation system. The method includes receiving electrocardiogram data output by multiple devices; performs data analysis on the electrocardiogram data, and generating report data and stores same; receiving a report data query by a user, queries corresponding report data according to a user ID of the user, and generating report result list for display and output; receiving a selection by the user, and obtaining selected report data according to the selection; receiving a report data consultation request input by the user; obtaining a user ID of an associated user corresponding to the user ID according to the report data consultation request, and sending the report data to a user equivalent of the associated user according to the user ID of the associated user; and receiving a consultation result feedback data sent by the user equivalent of the associated user.

Monitors and display screen switching methods therefor are disclosed. An exemplary monitor includes a host and a first display communicatively connected to the host. An exemplary display screen switching method for the monitor includes detecting that a second display is connected, the second display being provided independently of the monitor. The display screen switching method further includes detecting that a display screen switching instruction is received and reading a corresponding display file according to configuration parameters of the second display, the display file comprising one or more physiological parameters to be displayed, an interface layout and interface elements. The display screen switching method also includes acquiring data of the one or more physiological parameters according to the display file and generating frame data, the frame data being used for representing pixel values of pixels on a display interface. The display screen switching method additionally includes outputting the frame data to the second display to display data of the one or more physiological parameters.

An artificial intelligence self-learning-based static electrocardiography analysis method and apparatus, the method comprising data preprocessing, heartbeat detection, heartbeat classification based on a depth learning method, heartbeat verification, heartbeat waveform feature detection, measurement and analysis of electrocardiography events, and finally automatic output of reporting data, realizing an automated static electrocardiograph analysis method having a complete and rapid flow. The static electrocardiography analysis method can also record modification information of an automatic analysis result, collect modified data, and feed same back to the depth learning model to continue training, thereby continuously making improvements and improving the accuracy of the automatic analysis method.

A method for non-invasive detection of coronary artery disease (CAD). The method includes acquiring a raw ECG signal from a patient, generating a denoised ECG signal by applying a first wavelet transform on the raw ECG signal, generating an artifact-free ECG signal by applying a second wavelet transform on the denoised ECG signal, generating a filtered ECG signal by applying a band-stop filter on the artifact-free ECG signal, extracting an averaged ECG signal of a plurality of averaged ECG signals from the filtered ECG signal, detecting an ST segment in the averaged ECG signal by applying a delineation algorithm on the averaged ECG signal, detecting an isoelectric line in the averaged ECG signal, determining an existence of CAD in the patient responsive to detecting a CAD detection condition, and determining a non-existence of CAD responsive to not detecting the CAD detection condition.

A system for interpreting electrocardiograms receives a first electrocardiogram waveform, and identifies a feature in the first electrocardiogram waveform. The system retrieves one or more second electrocardiogram waveforms recorded at a point in time prior to the first electrocardiogram waveform. The system extracts the feature from the one or more second electrocardiogram waveforms, and generates one or more interpretive statements based on a comparison of the feature identified in the first electrocardiogram and the feature extracted from the one or more second electrocardiogram waveforms.

A system for interpreting electrocardiograms receives a first electrocardiogram waveform, and identifies a feature in the first electrocardiogram waveform. The system retrieves one or more second electrocardiogram waveforms recorded at a point in time prior to the first electrocardiogram waveform. The system extracts the feature from the one or more second electrocardiogram waveforms, and generates one or more interpretive statements based on a comparison of the feature identified in the first electrocardiogram and the feature extracted from the one or more second electrocardiogram waveforms.

A method and an apparatus for determining a coronary heart disease probability are provided. The method includes: obtaining ECG signals of one or more leads of a user; determining, based on the ECG signals of the one or more leads, reconstructed signals respectively corresponding to the ECG signals of the one or more leads; and determining a coronary heart disease probability of the user based on the ECG signals of the one or more leads and the reconstructed signals respectively corresponding to the ECG signals of the one or more leads.