Abstract: The present invention relates to a method for estimating blood pressure from photoplethysmography (PPG) signals. The blood pressure estimation method using the CFR model according to an embodiment of the present invention is characterized in that it comprises the steps of extracting a plurality of blood flow characteristics from PPG signals for training, calculating systolic and diastolic blood pressures from ambulatory blood pressures for training, labeling the systolic and diastolic blood pressures with the plurality of blood flow characteristics to train a cascade forest regression model, and inputting the PPG signals of a target user into the trained cascade forest regression model to determine the systolic and diastolic blood pressures of the target user.

Abstract: The present disclosure relates to a method of reconstructing an arterial blood pressure (ABP) signal corresponding to the morphological feature of a combination signal of ECG and PPG on the basis of the morphological feature. The method of reconstructing an ABP signal according to an embodiment of the present disclosure includes: generating an ECG-PPG signal by combining ECG and PPG signals corresponding to an ABP signal; generating a plurality of ECG-PPG signals for learning by applying a plurality of noises, which is different in intensity, to the ECG-PPG signal; training a neural network model to receive the ECG-PPG signals for learning and output the ABP signal; and reconstructing an ABP signal of a target user by inputting an ECG-PPG signal of the target user into the neural network model.

Abstract: The present disclosure relates to a method of extracting spatiotemporal features of electrocardiogram (ECG) and photoplethysmography (PPG) signals corresponding to each other using a neural network and of estimating blood pressure on the basis of the spatiotemporal features. The method includes: generating a target signal of a plurality of channels by respectively combining ECG signals and PPG signals corresponding to each other; extracting a first feature composed of a plurality of channels by inputting the target signal of a plurality of channels into a 1D convolution layer; generating a channel-wise weight vector by compressing the first feature; computing a second feature composed of a plurality of channels by applying the channel-wise weight vector to the first feature; extracting a third feature by inputting the second feature into a CNN model; and determining systolic and diastolic blood pressures by inputting the third feature into an LSTM model.

Abstract: The present invention relates to a method for estimating blood pressure from photoplethysmography (PPG) signals. The blood pressure estimation method using the CFR model according to an embodiment of the present invention is characterized in that it comprises the steps of extracting a plurality of blood flow characteristics from PPG signals for training, calculating systolic and diastolic blood pressures from ambulatory blood pressures for training, labeling the systolic and diastolic blood pressures with the plurality of blood flow characteristics to train a cascade forest regression model, and inputting the PPG signals of a target user into the trained cascade forest regression model to determine the systolic and diastolic blood pressures of the target user.

Abstract: An apparatus for predicting blood pressure non-compressively includes a sequence folding layer configured to convert a sequence image of non-pressurized biosignals into an arrayed image; a CNN layer configured to generate a feature map by performing a convolution operation on an arrayed image; a sequence unfolding layer configured to convert the generated feature map into a sequence image; a flatten layer configured to convert the converted sequence image into one-dimensional data; a long-short-term memory network layer configured to extract feature values from the converted one-dimensional data using weights; a fully-connected layer configured to perform image classification using feature values extracted from the long-short memory network layer; and a regression layer configured to predict systolic blood pressure (SBP) and diastolic blood pressure (DBP) for the classified image.