A system is described for predicting at least one cardiological dysfunction in an individual, having a means for providing an ECG which has a number n of time-synchronized ECG traces, each comprising a chronological sequence of time signals representing a sinus rhythm of the individual's heartbeat, to which at least one P wave, a QRS complex and a T wave can be assigned in chronological order. A selection means selects at least two ECG traces from the n ECG traces, an analysis unit analyses the selected ECG traces as follows: a) determining an isoelectric signal level, b) determining a first point in time chronologically before the QRS complex, c) determining a second point in time chronologically after the first point in time and chronologically before the QRS complex, d) carrying out the determining steps a) to c) for all selected ECG traces, e) determining an earliest first point in time from all the first points in time determined for the respective selected ECG traces and a latest second point in time from all the second points in time determined for the respective selected ECG traces, f) determining a time interval delimited by the earliest first point in time and latest second point in time.

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.

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.

The Cardiac Tissue Engineered Model (TEEM) invention provides a robust in vitro model for cardiotoxicity evaluation using three-dimensional (3D) human heart microtissues to quantify dose-dependent changes in electromechanical activity, resulting in a comprehensive cardiotoxicity and arrhythmia risk assessment of test compounds. The invention also provides a predictive in vitro screening platform for pro-arrhythmic toxicity testing using human three-dimensional cardiac microtissues. The invention enables the screening of environmental and pharmaceutical compounds, chemicals, and toxicants to establish safe human exposure levels.

Systems, methods, devices, and techniques for analyzing and applying features of a T-wave derived from an electrocardiogram. A computing system can receive a set of data that characterizes an electrocardiogram of a patient. The system can analyze the set of data to identify a T-wave that occurs in the electrocardiogram. The system can determine values of one or more features of the T-wave and provide the information that identifies the values of the one or more features of the T-wave to a user.

Systems, methods, devices, and techniques for analyzing and applying features of a T-wave derived from an electrocardiogram. A computing system can receive a set of data that characterizes an electrocardiogram of a patient. The system can analyze the set of data to identify a T-wave that occurs in the electrocardiogram. The system can determine values of one or more features of the T-wave and provide the information that identifies the values of the one or more features of the T-wave to a user.

A method for automatically and independently associating a cardiovascular disease with an electrocardiogram, ECG, signal includes receiving the ECG signal; denoising the ECG signal using a wavelet transform; determining peaks of the denoised ECG signal by applying a combination of (1) two event-related moving averages and (2) fractional-Fourier-transform (FrFT) to the denoised ECG signal; and passing the peaks through a classifier for identifying the cardiovascular disease that corresponds to the ECG signal.

A method for automatically and independently associating a cardiovascular disease with an electrocardiogram, ECG, signal includes receiving the ECG signal; denoising the ECG signal using a wavelet transform; determining peaks of the denoised ECG signal by applying a combination of (1) two event-related moving averages and (2) fractional-Fourier-transform (FrFT) to the denoised ECG signal; and passing the peaks through a classifier for identifying the cardiovascular disease that corresponds to the ECG signal.

An apparatus for measuring the size of an electrocardiography signal using a Hilbert transform, according to one embodiment of the present invention, may comprise: a receiving unit for receiving a measured electrocardiography signal; a transform unit for Hilbert-transforming the received electrocardiography signal; and a measurement unit for obtaining the size of the electrocardiography signal on the basis of the Hilbert-transformed electrocardiography signal.

An apparatus for measuring the size of an electrocardiography signal using a Hilbert transform, according to one embodiment of the present invention, may comprise: a receiving unit for receiving a measured electrocardiography signal; a transform unit for Hilbert-transforming the received electrocardiography signal; and a measurement unit for obtaining the size of the electrocardiography signal on the basis of the Hilbert-transformed electrocardiography signal.