Imedisync, Inc.

Lg Chem, Ltd

Chung Ang University Industry Academic Cooperation

The Catholic University of Korea Industry-academic Cooperation Foundation

Em-tech Co., Ltd.

Imedisync, Inc. Et Al.

Seung Wan Kang

Tae-gyun Jeong

Bo Hyon Sim

Jong Myung Kim

Ukeob Park

Namheon Kim

Byung Gyu Kim

Sung Wook Kim

Hyun Seo Park

Chang Seok Lee

Young Kwan Kim

Eun Sil Choi

Sang Dae Lee

Seung Yup Paek

Hyun Woo Joo

Sang Yun Park

Dong Won Yang

Young Chul Youn

Hyerin Jeong

Ukebo Park

Ju Hee Ko

Su Jin Yeo

Hye Won Ahn

Ji Ho Hong

Hee Dong Park

The present disclosure relates to an EEG measuring device which is in close contact with a head of a subject while having an adjustable size to fit the head of the subject.

EEG measuring device

Disclosed is a computer program stored in a computer-readable storage medium for achieving the above-described objects. When the computer program is executed by one or more processors, the computer program causes the one or more processors to perform the following operations of generating video data for diagnosing a health condition and a pathological symptom on the basis of a biosignal. The operations include receiving a biosignal of a user, preprocessing the biosignal to generate a plurality of pieces of video sub-data, and selecting at least two of the plurality of pieces of video sub-data to generate one or more pieces of video data.

Computer program and method for training artificial neural network model based on time-series biosignal

An automatic noise signal interval detection method and device are provided, and the method includes receiving input data and generating initial data in a time-frequency domain, calculating power for each epoch for each channel of the initial data, generating a power graph for a specific frequency region of each channel based on the power for each epoch, generating a baseline based on an average of each channel value on the power graph, and determining, as a noise signal interval, an interval exceeding a predetermined threshold based on the baseline on the power graph.

Automatic noise signal interval detection method and device

The present disclosure relates to a machine learning method and apparatus using steps feature selection based on a genetic algorithm, and the machine learning method includes defining a feature set including a plurality of features, generating a plurality of feature combinations including n-dimensional features (n is a natural number) for the feature set, independently constructing feature models for the plurality of feature combinations and calculating prediction accuracy for each of the feature models as a prediction result for a predetermined data set, arranging the feature models according to the prediction accuracy to determine at least one good feature model that satisfies a preset criterion, determining at least one good feature from among features included in a corresponding feature set of the at least one good feature model, and updating the feature set to include only the at least one good feature and re-determining a good feature model for a (n+1)-dimensional feature combination based on the updated feature set.

Machine learning method and apparatus using steps feature selection based on genetic algorithm

Provided is a cognitive impairment diagnosis method including: receiving an electroencephalogram signal of a user by a cognitive impairment diagnosis device; preprocessing the electroencephalogram signal by the cognitive impairment diagnosis device; extracting features, by the cognitive impairment diagnosis device, from the electroencephalogram signal by using a brain connectivity-based analysis method; and outputting cognitive impairment diagnosis information of the user by the cognitive impairment diagnosis device, on the basis of features having a causal relationship with the cognitive impairment diagnosis information, among the extracted features.

Method for diagnosing cognitive disorder, and computer program

Provided is a method for classifying severe cognitive impairment patients by analyzing electroencephalogram (EEG) data. The method of classifying severe cognitive impairment patients by analyzing EEG data includes a brainwave collection step of collecting EEG data on a plurality of users, a first classification step of classifying the plurality of users into a severe cognitive impairment group or a non-severe cognitive impairment group by analyzing the collected EEG data, a second classification step of classifying users included in the non-severe cognitive impairment group into a normal group or an amnestic mild cognitive impairment (aMCI) group, and a third classification group of classifying users included in the normal group into a within normal limits (WNL) group or a preclinical Alzheimer's disease (AD) group and classifying users included in the aMCI group into a non-AD MCI group or a prodromal AD group.

Method, server, and computer program for classifying severe cognitive impairment patients by analyzing EEG data

Compounds having the chemical formula 1, a method for producing the compounds of chemical formula 1, a pharmaceutical composition comprising same, and use thereof as a GPR120 agonist for prevention or treatment of inflammation or metabolic diseases such as diabetes, complications from diabetes, obesity, non-alcoholic fatty liver disease, fatty liver disease, and osteoporosis.

Biaryl derivative as GPR120 agonist

Growing community of inventors

Seoul, South Korea

Seung Wan Kang