Design of Deep Learning-based PPG Blood Pressure Prediction Model

Blood pressure is an important physiological signal of the human body and serves as a crucial reference standard for the prevention of cardiovascular diseases, making blood pressure monitoring particularly significant. In response to the current challenges of blood pressure measurement, such as difficulty and insufficient accuracy, this paper utilizes Photoplethymography (PPG) to acquire physiological parameters and combines deep learning algorithms to predict blood pressure values. First, the PPG signals in the database are preprocessed by denoising and cutting single-cycle signals, followed by feature extraction. Finally, a convolutional neural network (CNN) algorithm is used to build a PPG-based blood pressure prediction model. Experimental results show that the CNN model proposed in this paper has a mean error (ME) of $\mathbf{9 4 9 5} \mathbf{~ m m H g}$ for systolic blood pressure (SBP) and 7347 mmHg for diastolic blood pressure (DBP), both within the ±5mmHg range. Additionally, in terms of standard deviation (SD) for SBP and DBP, the CNN model has values of 4.0165 mmHg and 2.7941 mmHg, respectively, both less than 8 mmHg. The results meet the international standards of AAMI, indicating high accuracy and reliability in practical applications.