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公开(公告)号：US20210007618A1
公开(公告)日：2021-01-14
申请号：US16766569
申请日：2018-04-18
发明人： Jun Cao , Chuanyan Hu , Tao Liu , Yan Jiang , Liang Tian , Huili Cao , Chang Liu
IPC分类号： A61B5/04 , A61B5/0464 , A61B5/00 , A61B5/0472 , A61B5/0468
摘要： 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.
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2.发明申请公开(公告)号：US20200260979A1
公开(公告)日：2020-08-20
申请号：US16651890
申请日：2018-01-12
发明人： Jun Cao , Kaifeng Zang , Youchao LV , Pengfei Zhao , Erbin Wang , Chang Liu
IPC分类号： A61B5/04 , A61B5/00 , A61B5/0468 , A61B5/0472 , A61B5/0456
摘要： 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.
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公开(公告)号：US20200253494A1
公开(公告)日：2020-08-13
申请号：US16755113
申请日：2018-01-12
发明人： Kaifeng Zang , Haitao Lu , Pengfei Zhao , Yan Jiang , Baoquan Wang , Jun Cao
IPC分类号： A61B5/04 , A61B5/0245 , A61B5/024 , G16H50/20 , G16H15/00
摘要： 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 conclusion data.
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4.发明申请公开(公告)号：US20200237246A1
公开(公告)日：2020-07-30
申请号：US16755105
申请日：2018-01-12
发明人： Chuanyan Hu , Xue Zhang , Liang Tian , Tao Liu , Jun Cao , Chang Liu
IPC分类号： A61B5/0452 , G16H40/63 , G06N3/08 , G06K9/00
摘要： 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. The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.
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5.发明申请公开(公告)号：US20200260980A1
公开(公告)日：2020-08-20
申请号：US16651912
申请日：2018-01-12
发明人： Chang Liu , Chuanyan Hu , Weiwei Zhou , Haitao Lu , Jiayu Wang , Jun Cao
IPC分类号： A61B5/04 , A61B5/00 , A61B5/0472 , A61B5/0468 , A61B5/0456
摘要： 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.
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公开(公告)号：US20210280281A1
公开(公告)日：2021-09-09
申请号：US16766551
申请日：2018-04-18
发明人： Chuanyan Hu , Jun Cao , Chang Liu , Zifang Zhao , Xue Zhang , Baoquan Wang
摘要： 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.
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