# US 2025/0143592 A1 · Blood pressure estimation method based on spatiotemporal features of electrocardiogram and photoplethysmography > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0143592 A1 - Document ID: US20250143592A1 - Status: Published application - Kind: A1 - Application number: 18/766,168 - Filed: 2024-07-08 - Published: 2025-05-08 - Earliest claimed: 2023-11-07 - Assignees: Industry-Academic Cooperation Foundation, Chosun University - Applicants: Industry-Academic Cooperation Foundation, Chosun University - Inventors: Jae Hyo JUNG; Geng Jia ZHANG; Dae Gil CHOI - Web page: https://patentgrep.com/patent/US20250143592A1 ## CPC - A61B 5/02108: {from analysis of pulse wave characteristics} - A61B 5/02416: {using photoplethysmograph signals, e.g. generated by infrared radiation (A61B5/14552 takes precedence)} - A61B 5/352: Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval - A61B 5/7267: {involving training the classification device} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## 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. ## Claims ### Claim 1 A blood pressure estimation method based on spatiotemporal features of electrocardiogram (ECG) and photoplethysmography (PPG) signals, the blood pressure estimation method comprising: generating, by a processor, a target signal of a plurality of channels by respectively combining ECG signals and PPG signals corresponding to each other; extracting, by a processor, 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, by a processor, a channel-wise weight vector by compressing the first feature; computing, by a processor, a second feature composed of a plurality of channels by applying the channel-wise weight vector to the first feature; extracting, by a processor, a third feature by inputting the second feature into a CNN model; and determining, by a processor, systolic and diastolic blood pressures by inputting the third feature into an LSTM model. ### Claim 2 (depends on 1) The blood pressure estimation method of claim 1, further comprising: computing, by a processor, systolic and diastolic blood pressures for learning from an ambulatory blood pressure corresponding to an ECG signal for learning and a PPG signal for learning; generating, by a processor, a training dataset by labeling the systolic and diastolic blood pressures for learning on a signal obtained by combining the ECG signal for learning and the PPG signal for learning; and applying, by a processor, supervised learning to the 1D convolution layer, the CNN model, and the LSTM model using the training dataset. ### Claim 3 (depends on 2) The blood pressure estimation method of claim 2, wherein end-to-end learning is applied to the 1D convolution layer, the CNN model, and the LSTM model by the training dataset. ### Claim 4 (depends on 1) The blood pressure estimation method of claim 1, wherein the generating of a target signal includes removing noises in the ECG signal and the PPG signal by passing the ECG signal and the PPG signal through a Band Pass Filter (BPF). ### Claim 5 (depends on 1) The blood pressure estimation method of claim 1, wherein the generating of a target signal includes: detecting an R peak in the ECG signal; detecting a maximum peak in the PPG signal; aligning the ECG signal and the PPG signal on the basis of a time offset between the R peak and the maximum peak; and generating the target signal by combining the aligned ECG signal and PPG signal. ### Claim 6 (depends on 1) The blood pressure estimation method of claim 1, wherein the generating of a target signal includes: detecting a plurality of R peaks in an ECG signal in a unit time period; detecting a plurality of maximum peaks in a PPG signal in the unit time period; computing a time offset between an R peak and a maximum peak of a preset order of the plurality of R peaks and the plurality of maximum peaks; aligning the ECG signal and the PPG signal by applying the time offset to the ECG signal or the PPG signal; and generating the target signal by combining the aligned ECG signal and PPG signal. ### Claim 7 (depends on 1) The blood pressure estimation method of claim 1, wherein the generating of a target signal includes generating the target signal by time-serially connecting the PPG signal to the ECG signal. ### Claim 8 (depends on 1) The blood pressure estimation method of claim 1, wherein the extracting of a first feature includes extracting the first feature including temporal information by time-serially convoluting a 1D kernel to the target signal. ### Claim 9 (depends on 1) The blood pressure estimation method of claim 1, wherein the generating of a channel-wise weight vector includes: extracting a global feature including spatial information by squeezing the first feature; and generating the weight vector composed of elements having a value between 0 and 1 by scaling the global feature. ### Claim 10 (depends on 1) The blood pressure estimation method of claim 1, wherein the computing of a second feature includes computing the second feature by performing element-wise product on the channel-wise weight vector and the first feature. ### Claim 11 (depends on 1) The blood pressure estimation method of claim 1, wherein the CNN model extracts the third feature including spatial information from the second feature through several pairs of convolution layers and pooling layers. ### Claim 12 (depends on 1) The blood pressure estimation method of claim 1, wherein the LSTM model extracts a fourth feature including spatial information from the third feature. ### Claim 13 (depends on 1) The blood pressure estimation method of claim 1, wherein the determining of systolic and diastolic blood pressures includes: inputting the third feature into the LSTM model; inputting output of the LSTM model into a Fully Connected Layer (FCL); and determining the systolic and diastolic blood pressures in accordance with two pieces of output of the fully connected layer.