Acer Inc.

National Yang Ming Chiao Tung University

Taipei Veterans General Hospital

Acer Medical Inc.

Chung Yuan Christian University

Hao-Min Cheng

Tsung-Hsien Tsai

Chun-Hsien Li

Chen-Huan Chen

Wei-Ting Wang

Jun-Hong Chen

Yin-Hao Lee

Liang-Kung Chen

Wei-Chih Hu

Liang-Yu Shyu

Yuan-Ta Shih

Yi-Jung Sun

Shih-Hsien Sung

A feature identifying method and an electronic device are provided. The method includes: obtaining a plurality of physiological information obtained by measuring a subject at a plurality of time points in one day; converting the plurality of physiological information into a plurality of correlation features respectively; establishing a plurality of first risk prediction models according to the plurality of correlation features, and identifying at least one first correlation feature from the plurality of correlation features according to the plurality of first risk prediction models; establishing a plurality of second risk prediction models according to the at least one first correlation feature, and identifying, according to the plurality of second risk prediction models, at least one second correlation feature capable of predicting a specific disease from the at least one first correlation feature; and outputting the at least one second correlation feature.

Feature identifying method and electronic device

The disclosure provides a disease classification method and a disease classification device. The disease classification method includes: inputting samples into a first stage model and obtaining a first stage determination result; inputting first samples determined positive by the first stage model into a second stage high specificity model to obtain second samples determined to be positive and third samples determined to be negative and rule in the second samples; inputting fourth samples determined negative by the first stage model into a second stage high sensitivity model to obtain fifth samples determined to be positive and sixth samples determined to be negative and rule out the sixth samples; obtaining a second stage determination result of the second and sixth samples; and inputting the third and fifth samples not ruled in or ruled out into a third stage model and obtaining a third stage determination result of the third and fifth samples.

Disease classification method and disease classification device

A physiological status evaluation method and a physiological status evaluation apparatus are provided. The method includes the following: obtaining original electrocardiogram data of a user by an electrocardiogram detection apparatus; converting the original electrocardiogram data into digital integration data; obtaining a plurality of physiological characteristic parameters according to the digital integration data; filtering the physiological characteristic parameters for at least one notable characteristic parameter through at least one filter model, where decision importance of the at least one notable characteristic parameter in a decision process of the at least one filter model is greater than a threshold; building a prediction model according to the at least one notable characteristic parameter; and evaluating a physiological status of the user through the prediction model.

Physiological status evaluation method and physiological status evaluation apparatus

A method and device for estimating central aortic pulse pressure by cuff pressure pulse wave oscillation signals. The method comprises (I) providing an equation for estimating central aortic pulse pressure; (II) measuring pressure pulse wave oscillation signals in a cuff, which comprise a brachial systolic blood pressure, a diastolic blood pressure and a waveform of the pressure pulse wave oscillation signals in the cuff; (III) averaging the waveform of the pressure pulse wave oscillation signals and calibrating the averaged waveform; (IV) analyzing the calibrated pressure pulse wave oscillation signal waveform to obtain a plurality of predicting variables; and (V) obtaining an estimate of the central aortic pulse pressure by values of predicting variables in step (IV) and the equation in step (I).

Method for estimating central aortic pulse pressure by cuff pressure pulse wave oscillation signals and device thereof

A method for detecting vascular sclerosis is revealed. Firstly set a cuff on a human hand. Then inflate the cuff and later deflate the cuff. Next measure a pressure of the cuff and generate a pressure sensing signal. Process the pressure sensing signal to generate a processed signal and convert the processed signal. Then calculate a systolic pressure and a diastolic pressure according to the converted processed signal and also obtain a vasodilation constant. Thus a hardening of blood vessels is detected according to the vasodilation constant.

Method for detecting vascular sclerosis

Growing community of inventors

Taipei, Taiwan

Hao-Min Cheng