公开(公告)号：US20220309268A1

公开(公告)日：2022-09-29

申请号：US17513473

申请日：2021-10-28

Applicant: BOE TECHNOLOGY GROUP CO., LTD.

Inventor： Xue CHEN ,  Jinnv LIU ,  Yude LI ,  Xianqiang NI ,  Shuobin LIANG ,  Li ZHOU

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/52 ,  G06K9/20 ,  G16H10/60 ,  G16H50/20 ,  A61B5/30 ,  A61B5/308

Abstract: An attention mechanism-based 12-lead electrocardiogram (ECG) classification method is described, the method including acquiring an original image of a 12-lead ECG, segmenting waveform data recorded in the original image to obtain segmented waveform data for each lead in the 12-lead ECG, performing depth feature extraction on the segmented waveform data of said each lead to obtain a first feature map of said each lead, performing feature transformation on the first feature map of said each lead based on an attention mechanism to obtain a depth feature of said each lead, and classifying the depth feature of said each lead to obtain a classification result for the original image. The classification method can make full use of the 12-lead ECG for overall classification and improve the accuracy of image classification.

公开(公告)号：US20230309899A1

公开(公告)日：2023-10-05

申请号：US17912643

申请日：2021-06-10

Applicant: BOE TECHNOLOGY GROUP CO., LTD.

Inventor： Xue CHEN ,  Jinnv LIU ,  Yude LI ,  Zheng WU ,  Li ZHOU

IPC: A61B5/355 ,  A61B5/352 ,  A61B5/00

CPC classification number: A61B5/355 ,  A61B5/352 ,  A61B5/726

Abstract: A determination method for a beginning of T-wave includes: obtaining an electrocardiogram (ECG) signal; identifying a form of each T-wave in the ECG signal; and calling a preset algorithm corresponding to the form of the T-wave according to the form of the T-wave to determine a beginning of the T-wave.

公开(公告)号：US20220218261A1

公开(公告)日：2022-07-14

申请号：US17605278

申请日：2020-09-28

Applicant: BOE TECHNOLOGY GROUP CO., LTD. ,  Peking University

Inventor： Li JIANG ,  Yongyue SUN ,  Siyang LIANG ,  Meng WU ,  Yude LI ,  Hongyan LI ,  Litong HAN

IPC: A61B5/35 ,  A61B5/366 ,  A61B5/364

Abstract: A method for processing time series data comprises: dividing time series data into a plurality of data fragments according to an objective function, the plurality of data fragments having a greatest similarity; in response to determining that at least one data fragment does not satisfy an iteration termination condition, performing following iteration operations on the at least one data fragment; and constructing a time series base pattern library by using a plurality of time series base patterns. The iteration operations includes: using the at least one data fragment as at least one update time series fragment; dividing each update time series fragment into a plurality of update data fragments; using each update data fragment that does not satisfy the iteration termination condition as a new update time series fragment; and using each update data fragment that satisfies the iteration termination condition as a time series base pattern.