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.