Abstract: An ECG information processing method and ECG workstation, wherein the method comprises: the ECG workstation receives the ECG data output by an ECG device, the ECG data includes ID of the measured object and the detection time information; perform ECG data analysis on the ECG data to generate report data; receive a report data query input by the user, query corresponding report data according to the user ID of the user, and generate report data query result list data; acquire the selected report data according to the selection instruction, determine output mode information and output format information according to the report output, and, selectively output report conclusion data, report entry data, and/or the partial data or all the data in the report graphic data, and convert the partial data or all the data in data format according to the output format information, to generate report output data.

Abstract: An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data; cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data; performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array; performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data; and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information.

Abstract: An artificial intelligence self-learning-based automatic electrocardiography analysis method and apparatus, the method comprising data preprocessing, heartbeat feature detection, interference signal detection and heartbeat classification based on deep learning, signal quality evaluation and lead combination, heartbeat verification, analysis and calculation of electrocardiography events and parameters, and finally automatic output of reporting data, realizing an automated analysis method having a complete and rapid flow. The automatic electrocardiography analysis method may 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.

Abstract: An electrocardiogram information dynamic monitoring method and dynamic monitoring system.

Abstract: A detection report data generation method including acquiring event type information of an electrocardiogram event corresponding to electrocardiogram event data, wherein the event data has one or more pieces of event type information; screening the event data according to signal quality evaluation indexes so as to obtain report conclusion data and report entry data; carrying out quality assessment on an event segment included in the event data according to the signal quality evaluation indexes, and determining a pre-selected sample segment according to a quality assessment result; determining position information of an event heart beat in the pre-selected sample segment, and determining segment interception parameters; carrying out interception processing on the pre-selected sample segment according to the segment interception parameters so as to obtain a typical data segment; generating report graphic data according to the typical data segment; and outputting the entry data, the graphic data and the conclusi

Abstract: 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.

Abstract: 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.

Abstract: An artificial intelligence-based interference recognition method for an electrocardiogram, comprising: cutting and sampling heart beat data of a first data amount, and inputting the heart beat data to be recognized that is obtained by cutting and sampling into an interference recognition binary classification model for interference recognition; in a sequence of the heart beat data, performing signal anomaly determination on a heart beat data segment where an inter-beat interval is greater than or equal to a preset interval determination threshold value, so as to determine whether the heart beat data segment is an abnormal signal; if the heart beat data segment is not an abnormal signal, determining a starting data point and an ending data point of sliding sampling in the heart beat data segment according to a set time with a preset time width, and performing sliding sampling on the data segment from the starting data point until the ending data point so as to obtain multiple sampling data segments; and using

Abstract: A method for assessing electrocardiogram signal quality, the method comprising: receiving heart beat analysis data by processing electrocardiogram data acquired from an electrocardiogram monitoring device; extracting position information and width information of a QRS complex in the heart beat analysis data; extracting an RR interval signal between two adjacent QRS complex signals; performing QRS complex signal cancellation processing on the RR interval signal to obtain an RR interval signal for which the QRS complex signal is removed; filtering the processed RR interval signal without the QRS complex signal, and performing envelope calculation on the filtered signal to obtain the average power of a noise signal of the RR interval signal for which the QRS complex signal is removed; obtaining a signal quality evaluation index according to the average power of the noise signal and the power of the QRS complex signal.

Abstract: A self-learning dynamic electrocardiography analysis method employing artificial intelligence. The method comprises: pre-processing data, performing cardiac activity feature detection, interference signal detection and cardiac activity classification on the basis of a deep learning method, performing signal quality evaluation and lead combination, examining cardiac activity, performing analytic computations on an electrocardiogram event and parameters, and then automatically outputting report data. The method achieves an automatic analysis method for a quick and comprehensive dynamic electrocardiography process, and recording of modification information of an automatic analysis result, while also collecting and feeding back modification data to a deep learning model for continuous training, thereby continuously improving and enhancing the accuracy of the automatic analysis method. Also disclosed is a self-learning dynamic electrocardiography analysis device employing artificial intelligence.