基于二维卷积的连续血压预测系统OACSTPCD

To address the demands of digital acquisition of vital signs signals and continuous blood pressure prediction,this paper designs and constructs a continuous blood pressure prediction system based on two-dimensional(2D)convolution.The system hardware adopts ESP32 module,AD8232 module and PulseSensor sensor to collect the human electrocardiography(ECG)and photoplethysmography(PPG)signal data,which are then transmitted to the server through the MQTT protocol for the consequent processing.Regarding the algorithms of this paper,a neural network model using ECG and PPG signals was designed and trained to predict continuous human blood pressure,employing the Gramian angular difference field(GADF),2D convolution,and model pruning techniques.The performance of the continuous blood pressure prediction model is verified on both classic open-source datasets and self-collected datasets.This system proposed in this paper provides a practical reference scheme for the vital signs signal acquisition and continuous blood pressure prediction.