Abstract: Disclosed is an apparatus for electrocardiogram (ECG) wave classification using machine learning being capable of applying a segmentation technique to an ECG wave, checking a feature for each section of a P wave, a Q wave, an R wave, an S wave, a T wave, and a noise wave included in the ECG wave, quickly classifying heart beats into a normal beat (N), a supraventricular beat(S), a ventricular beat (V), and noise, and removing the noise to make medical decisions quickly and accurately.
Type:
Application
Filed:
December 17, 2021
Publication date:
January 30, 2025
Applicant:
SEERSTECHNOLOGY CO.,LTD.
Inventors:
Youngshin LEE, Hoohyun KIM, Heeseok SONG, Kunwook CHO
Abstract: Disclosed is an atrial fibrillation discriminating apparatus using deep learning being capable of performing learning whether atrial fibrillation, which is a type of arrhythmia, occurs on the basis of deep learning, by applying a segmentation scheme to an ECG wave to quickly classify fibrillation waves on the basis of an ROI (Region of Interest) section in an ECG wave, thereby detecting the atrial fibrillation.
Type:
Application
Filed:
December 17, 2021
Publication date:
January 30, 2025
Applicant:
SEERSTECHNOLOGY CO.,LTD.
Inventors:
Youngshin LEE, Hoohyun KIM, Heeseok SONG, Kunwook CHO
Abstract: Disclosed is a body temperature measuring patch using an IR temperature sensor. The present embodiment provides a body temperature measuring patch using an infrared temperature sensor, which enables the body temperature to be measured more accurately and quickly when a body temperature is continuously monitored by applying a surface mounted device (SMD)-type infrared (IR) temperature sensor and thus implementing a patch-type thermometer.
Abstract: Disclosed are a method and an apparatus for multiple-beat detection using electrocardiogram global feature vectors. This method and apparatus extracts global features of each electrocardiogram wave, and extracts and learns, using the extracted global features as input vectors, a pattern of global features of a consecutive electrocardiogram wave by applying an attention mechanism to a weighted feature matrix in consideration of the degree of contribution of each feature to detect multiple beats.
Type:
Application
Filed:
December 23, 2021
Publication date:
February 1, 2024
Applicant:
SEERSTECHNOLOGY CO.,LTD.
Inventors:
Youngshin LEE, Heeseok SONG, Yunkwan KIM