公开(公告)号：US20240115184A1

公开(公告)日：2024-04-11

申请号：US18263102

申请日：2022-01-27

Applicant: VUNO Inc.

Inventor： Youngjae SONG ,  Woong BAE

IPC: A61B5/327 ,  A61B5/00 ,  A61B5/364 ,  G06N20/00 ,  G16H50/30

CPC classification number: A61B5/327 ,  A61B5/364 ,  A61B5/7239 ,  A61B5/7275 ,  G06N20/00 ,  G16H50/30

Abstract: According to an embodiment of the present disclosure, disclosed is a method for predicting a chronic disease based on an ECG signal performed by a computing device. The method may include generating lead-specific integrated data based on the ECG signal. The method may include generating N-dimensional input data based on the lead-specific integrated data. The method may include predicting the chronic disease based on the N-dimensional input data. The method may include generating prediction information on the chronic disease to be provided to a user.

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.

公开(公告)号：US20240366100A1

公开(公告)日：2024-11-07

申请号：US18291164

申请日：2022-08-10

Applicant: VUNO Inc.

Inventor： Youngjae SONG ,  Sungjae LEE

IPC: A61B5/024 ,  A61B5/00

Abstract: Disclosed is a method for extracting a heart rate variability (HRV) feature value performed by a computing device including one or more processors. The method includes acquiring first biosignal data measured during a first time period. The method includes outputting one or more heart rate variability feature values corresponding to a time period longer than the first time period by inputting the first biosignal data into a pre-trained neural network model.