公开(公告)号：US20220076835A1

公开(公告)日：2022-03-10

申请号：US17466736

申请日：2021-09-03

Applicant: VUNO Inc.

Inventor： Byeong Tak LEE ,  Woong BAE ,  Oyeon KWON

IPC: G16H50/20 ,  G06N3/08 ,  G06K9/62

Abstract: According to an embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium, in which when the computer program is executed on at least one processor, the computer program causes the processor to perform the following operations for judging a disease using a neural network, the operations including: acquiring one or more bio signals respectively measured in one or more leads; and generating result information about a disease by inputting the one or more bio signals into a disease judgment model.

公开(公告)号：US20210369172A1

公开(公告)日：2021-12-02

申请号：US17330102

申请日：2021-05-25

Applicant: VUNO Inc.

Inventor： Oyeon KWON ,  Woong BAE ,  Yeha LEE

IPC: A61B5/332 ,  A61B5/28 ,  A61B5/00

Abstract: Disclosed is a portable electrocardiogram measuring device for calculating one or more electrocardiogram leads according to an embodiment of the present disclosure. The device may include: a main measurement unit comprising a first electrode, a second electrode, and one or more processors; and a sub measurement unit comprising a third electrode, in which the one or more processors measure an electrocardiogram, by receiving electrical signals from at least two electrodes in a measurable state and by calculating different types of electrocardiogram leads based on the number of electrodes in the measurable state and an attachment position of electrodes.

公开(公告)号：US20240252091A1

公开(公告)日：2024-08-01

申请号：US18632087

申请日：2024-04-10

Applicant: VUNO Inc.

Inventor： Oyeon KWON ,  Woong BAE ,  Yeha LEE

IPC: A61B5/332 ,  A61B5/00 ,  A61B5/28

CPC classification number: A61B5/332 ,  A61B5/0006 ,  A61B5/28 ,  A61B5/7221

Abstract: Disclosed is a portable electrocardiogram measuring device for calculating one or more electrocardiogram leads according to an embodiment of the present disclosure. The device may include: a main measurement unit comprising a first electrode, a second electrode, and one or more processors; and a sub measurement unit comprising a third electrode, in which the one or more processors measure an electrocardiogram, by receiving electrical signals from at least two electrodes in a measurable state and by calculating different types of electrocardiogram leads based on the number of electrodes in the measurable state and an attachment position of electrodes.

公开(公告)号：US20220084679A1

公开(公告)日：2022-03-17

申请号：US17464685

申请日：2021-09-02

Applicant: VUNO INC.

Inventor： Byeongtak LEE ,  Youngjae SONG ,  Woong BAE ,  Oyeon KWON

IPC: G16H50/20 ,  G06K9/00 ,  G06N20/00 ,  G16H50/70 ,  A61B5/353 ,  A61B5/355 ,  A61B5/366 ,  A61B5/36

Abstract: A deep neural network pre-training method for classifying electrocardiogram (ECG) data and a device for the same are disclosed. A method for training an ECG feature extraction model may include receiving a ECG signal, extracting one or more first features related to the ECG signal by inputting the ECG signal to a rule-based feature extractor or a neural network model, extracting at least one second feature corresponding to the at least one first feature by inputting the ECG signal to an encoder, and pre-training the ECG feature extraction model by inputting the at least one second feature into at least one of a regression function and a classification function to calculate at least one output value. The pre-training of the ECG feature extraction model may include training the encoder to minimize a loss function that is determined based on the at least one output value and the at least one first feature.