Abstract: A semiconductor structure includes a composite redistribution structure, a first interconnect device and an integrated circuit (IC) package component. The composite redistribution structure includes a first redistribution structure, a second redistribution structure and a third redistribution structure. The second redistribution structure is located between the first redistribution structure and the third redistribution structure. The first interconnect device is embedded in the second redistribution structure. The first interconnect device includes a plurality of metal connectors leveled with a surface of the second redistribution structure and electrically connected to the third redistribution structure. The IC package component is disposed over the third redistribution structure and electrically connected to the first interconnect device via the third redistribution structure.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: The present invention relates to a central blood pressure estimation device (11).

Abstract: 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.

Abstract: A blood pressure monitor and a method for calculating blood pressure thereof are revealed. The blood pressure monitor includes a cuff, an air pump, an air escape valve, a pressure sensor, a processing circuit, and an arithmetic circuit. The cuff is arranged a a body to be detected while and the air pump inflates the cuff and the air escape valve is for releasing air from the cuff. The pressure sensor is disposed on the cuff for detecting cuff pressure to generate analog pressures sensing signals. The processing circuit processes the analog pressure sensing signals and generates digital pressure sensing signals. A slope of each digital pressure sensing signal is calculated by the arithmetic circuit. A pressure value of the digital pressure sensing signal corresponding to a maximum slope is an average blood pressure. Then find a second derivative of each digital pressure sensing signal.

Abstract: A blood pressure monitor and a method for detecting vascular sclerosis thereof are revealed. The blood pressure monitor includes a cuff, an air pump, an air escape valve, a pressure sensor, a processing circuit, and an arithmetic circuit. The cuff is arranged at a body to be detected and the air pump inflates the cuff. The air escape valve is for releasing air from the cuff while the pressure sensor arranged at the cuff measures a cuff pressure to generate an analog pressure sensing signal. The processing circuit processes the pressure sensing analog signal and generates a digital pressure sensing signal. Then a systolic pressure and a diastolic pressure of the detected body are calculated according to the digital pressure sensing signal. The degree of blood vessel hardening is checked according to a systolic area of the calculated systolic pressure and a diastolic area of the calculated diastolic pressure.

Abstract: 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.

Abstract: A blood pressure monitor that detects vascular sclerosis is revealed. The blood pressure monitor includes a cuff, an air pump, an air escape valve, a pressure sensor, a processing circuit, a first conversion circuit, and an arithmetic circuit. The cuff is arranged at a user's hand and is pumped up and inflated by the air pump while the air escape valve is used to release air from the cuff. The pressure sensor is disposed on the cuff to detect a pressure of the cuff and generate a pressure sensing signal. The processing circuit processes the pressure sensing signal and generates a processed signal that is converted by the first conversion circuit, According to the converted processed signal, the arithmetic circuit calculates a systolic pressure, a diastolic pressure of a user and obtains a vasodilation constant so as to check vascular sclerosis of the user.