Abstract: Provided are a system and a method for cardiovascular risk prediction, where artificial intelligence is utilized to perform segmentation on non-contrast or contrast medical images to identify precise regions of the heart, pericardium, and aorta of a subject, such that the adipose tissue volume and calcium score can be derived from the medical images to assist in cardiovascular risk prediction. Also provided is a computer readable medium for storing a computer executable code to implement the method.

Abstract: A feature extraction method for photoplethysmography includes obtaining a photoplethysmography (PPG) signal; calculating a first-order derivative photoplethysmography (FDPPG) signal, a second-order derivative photoplethysmography (SDPPG) signal and a third-order derivative photoplethysmography (TDPPG) signal from the PPG signal, wherein the SDPPG signal has multiple feature points; and performing a feature extraction operation that exploits the property of the TDPPG signal to impute the missing feature points of the SDPPG signal. Moreover, the feature extraction operation further includes: resolving the ambiguous feature points of the SDPPG signal.

Abstract: The present invention proposes a relaxation state evaluation system and method and a computer program product thereof. The method comprises steps: measuring ECG data of a user; analyzing the ECG data to generate a first, second, third and fourth parameters, wherein the first parameter is the short-scale entropy slope of the user before cardiovascular disease treatment (CVDT); the second parameter is the difference of the post-CVDT and pre-CVDT mean RR intervals; the third parameter is the logarithm of the variance of the pre-CVDT high frequency NN intervals; the fourth parameter is the logarithm of the ratio of the variances of the pre-CVDT low frequency and high frequency NN intervals; working out an evaluation index, which is a function of the abovementioned parameters; and evaluating the relaxation state of the user, wherein the user is determined to be in a relaxation state if the evaluation index is over a threshold.

Abstract: The present invention proposes a relaxation state evaluation system and method and a computer program product thereof. The method comprises steps: measuring ECG data of a user; analyzing the ECG data to generate a first, second, third and fourth parameters, wherein the first parameter is the short-scale entropy slope of the user before cardiovascular disease treatment (CVDT); the second parameter is the difference of the post-CVDT and pre-CVDT mean RR intervals; the third parameter is the logarithm of the variance of the pre-CVDT high frequency NN intervals; the fourth parameter is the logarithm of the ratio of the variances of the pre-CVDT low frequency and high frequency NN intervals; working out an evaluation index, which is a function of the abovementioned parameters; and evaluating the relaxation state of the user, wherein the user is determined to be in a relaxation state if the evaluation index is over a threshold.