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 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: 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.

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 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 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: 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.

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.