公开(公告)号：US11212672B2

公开(公告)日：2021-12-28

申请号：US17036222

申请日：2020-09-29

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Fangmin Sun ,  Ye Li

IPC: H04M1/66 ,  H04M1/68 ,  H04M3/16 ,  H04W12/0431 ,  H04W4/80 ,  H04W12/041 ,  H04L9/08 ,  H04W12/037

Abstract: The embodiments of the present disclosure are applicable to the technical field of computer science and application technology, and disclose a wireless body area network, a key generation method and a key distribution method in the wireless body area network, and a related device. The gait acceleration signal is extracted synchronously through the respective acceleration acquisition devices integrated with the coordinator and the wearable equipment, the position information corresponding to the peak value and the valley value in the gait acceleration signal is correspondingly extracted and is taken as the gait common information, and the gait common information is used to perform key distribution in the wireless body area network, the security and the consistency are higher, the calculation is simplified, the key distribution method is suitable for wearable devices having limited resources.

公开(公告)号：US11538588B2

公开(公告)日：2022-12-27

申请号：US16901033

申请日：2020-06-15

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Ye Li ,  Xiaomao Fan ,  Qihang Yao ,  Liyan Yin

IPC: G06N3/00 ,  G16H50/20 ,  A61B5/00 ,  G06N3/04 ,  G06N3/08 ,  A61B5/352 ,  A61B5/361

Abstract: The present disclosure provides an atrial fibrillation signal recognition method, apparatus and device. The method comprises: obtaining an electrocardiogram signal to be recognized; inputting the electrocardiogram signal to be recognized to a pre-established atrial fibrillation signal recognition model, and outputting an atrial fibrillation signal recognition result, where the atrial fibrillation signal recognition model is established in the following way: obtaining a specified number of electrocardiogram sample signals and corresponding identifier information; balancing, according to the number of normal signals, atrial fibrillation signals by means of SMOTE; establishing a network structure of multiple convolutional neural networks, each of the convolutional neural networks being provided with a specific receptive field for recognizing the atrial fibrillation signals of a corresponding granularity; and inputting the normal signals and the balanced atrial fibrillation signals to the network structure for training to generate an atrial fibrillation signal recognition model.

公开(公告)号：US12092714B2

公开(公告)日：2024-09-17

申请号：US17992868

申请日：2022-11-22

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Hairong Zheng ,  Qiaoyan Chen ,  Ye Li ,  Chao Luo ,  Xin Liu

IPC: G01R33/3875 ,  A61B5/055

CPC classification number: G01R33/3875 ,  A61B5/055

Abstract: A shimming method and device, an electronic device, and a storage medium are disclosed. The shimming method includes: obtaining object static magnetic field distribution information corresponding to a target object, the object static magnetic field distribution information including the static magnetic field distribution information of the target object under the action of a main magnet of a magnetic resonance system; determining a target static magnetic field based on the object static magnetic field distribution information and a preset shim coil magnetic field distribution model; and adjusting at least one shim coil parameter in the shim coil magnetic field distribution model until a magnetic field uniformity of the target static magnetic field satisfies a preset condition, and accordingly obtaining at least one target shim coil parameter.

公开(公告)号：US10743789B2

公开(公告)日：2020-08-18

申请号：US15739719

申请日：2017-11-28

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY, CHINESE ACADEMY OF SCIENCES

Inventor： Ye Li ,  Xiaomao Fan ,  Yunpeng Cai ,  Qihang Yao ,  Yujie Yang

IPC: A61B5/0452 ,  A61B5/04 ,  A61B5/0472 ,  A61B5/00 ,  A61B5/0404 ,  G16H50/20 ,  A61B5/0432 ,  A61B5/0245 ,  A61B5/0456

Abstract: Provided are an electrocardiogram signal parallel analysis apparatus, a mobile terminal incorporating the apparatus, and related methods. The apparatus includes an integrated memory, a central processing unit and a graphic processing unit. The integrated memory includes a first memory and a second memory for being used by the central processing unit and the graphic processing unit respectively, and the central processing unit may access the second memory. The central processing unit performs primary noise reduction on a received electrocardiogram original signal to obtain a primary electrocardiogram signal, and performs abnormal heartbeat classification preliminary screening on characteristic data extracted from the graphic processing unit to obtain suspected abnormal heartbeat data. The graphic processing unit performs characteristic extraction on the primary electrocardiogram signal to obtain characteristic data, performs secondary noise reduction on the primary electrocardiogram signal to obtain a secondary electrocardiogram signal, and processes the suspected abnormal heartbeat data and the secondary electrocardiogram signal by applying a template matching classification mode to obtain final abnormal heartbeat data.

公开(公告)号：US10258250B2

公开(公告)日：2019-04-16

申请号：US15389978

申请日：2016-12-23

Applicant: SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY CHINESE ACADEMY OF SCIENCES

Inventor： Ye Li ,  Xiaomao Fan ,  Furu Xiang ,  Yunpeng Cai ,  Fen Miao

IPC: A61B5/04 ,  A61B5/0456 ,  A61B5/00 ,  A61B5/0472

Abstract: The present disclosure provides a GPU-based parallel electrocardiogram signal analysis method, comprising: performing a filtering process of electrocardiogram signals through a long interval artifact removal and a short interval artifact removal; performing a QRS detection of the filtering-processed electrocardiogram signals through an R-wave position extraction, a QRS complex start and end positions extraction and a QRS complex width extraction; performing an abnormal waveform classification of the QRS-detected electrocardiogram signals through template creation; wherein at least one of the long interval artifact removal, the short interval artifact removal, the R-wave position extraction, the QRS complex width extraction and the creation template is performed by a multiple threads at a GPU device side in parallel, any thread being read through its unique index number to process corresponding data. By executing one or more steps of the electrocardiogram signal analysis at GPU in parallel, the present disclosure increases the analysis speed of the electrocardiogram signals.