Acer Inc.

National Yang Ming Chiao Tung University

Li-Ning Peng

Liang-Kung Chen

Tsung-Hsien Tsai

Yun-Hsuan Chan

Chun-Hsien Li

Pei-Jung Chen

Tsung-Han Yang

Chi-Hung Lin

Jun-Hong Chen

Andy Ho

Szu-Chieh Wang

Ting-Fen Tsai

Ke-Han Pan

Zong-Han Tsai

Chien-Yi Tung

Wei-Ju Lin

Shih-Tsung Huang

Fei-Yuan Hsiao

Wei-Ju Li

A method for analyzing an electrocardiography (ECG) signal is provided. The ECG signal is measured through a sensor. One or more specified waveforms in the ECG signal are detected. Multiple ECG features from each specified waveform are captured. The ECG features are input into multiple candidate models to obtain multiple candidate scores. Each candidate score is compared with a standard value to obtain an ideal score among the candidate scores. One of the candidate models corresponding to the ideal score is selected as a risk prediction model to predict disease risk through the risk prediction model.

Method for analyzing electrocardiography signal

A training data processing method and an electronic device are provided. The method includes: obtaining medical history data including at least one first disease suffered by a user; setting a plurality of disease types according to a target disease; setting a time interval; obtaining at least one second disease in the time interval from the medical history data; performing a pre-processing operation on the second disease according to the disease types to obtain processed data; and inputting the processed data to a neural network to train the neural network.

Training data processing method and electronic device

A method and a system for mental index prediction are provided. The method includes the following steps. A plurality of images of a subject person are obtained. A plurality of emotion tags of the subject person in the images are analyzed. A plurality of integrated emotion tags in a plurality of predetermined time periods are calculated according to the emotion tags respectively corresponding to the images. A plurality of preferred features are determined according to the integrated emotion tags. A mental index prediction model is established according to the preferred features to predict a mental index according to the emotional index prediction model.

Method and system for mental index prediction

A method and an electronic device for selecting influence indicators by using an automatic mechanism are provided. The method includes following steps. Raw data is obtained, where the raw data includes a body-related variable and a plurality of to-be-measured indicators corresponding to the body-related variable. The body-related variable is set as a target parameter. The body-related variable and the to-be-measured indicators are input into a plurality of validation models, and the to-be-measured indicators are sorted according an output result of the validation models to obtain ranking data. Importance of the to-be-measured indicators is calculated by using a screening condition according to the ranking data, so as to select a candidate indicator from the to-be-measured indicators. An influence indicator is determined by calculating a correlation between the candidate indicator and the body-related variable.

Method and electronic device for selecting influence indicators by using automatic mechanism

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Taipei, Taiwan

Li-Ning Peng