Prediction of blood pressure variability using deep neural networks

Purpose: The purpose of our study was to predict blood pressure variability from time-series data of blood pressure measured at home and data obtained through medical examination at a hospital. Previous studies have reported the blood pressure variability is a significant independent risk factor for cardiovascular disease. Methods: We adopted standard deviation for a certain period and predicted variabilities and mean values of blood pressure for 4 weeks using multi-input multi-output deep neural networks. In designing the prediction model, we prepared a dataset from a clinical study. The dataset included past time-series data for blood pressure and medical examination data such as gender, age, and others. As evaluation metrics, we used the standard deviation ratio (SR) and the root-mean-square error (RMSE). Moreover, we used cross-validation as the evaluation method. Results: The prediction performances of blood pressure variability and mean value after 1–4 weeks showed the SRs were “0.67” to “0.70”, the RMSEs were “5.04” to “6.65” mmHg, respectively. Additionally, our models were able to work for a participant with high variability in blood pressure values due to its multi-output nature. Conclusion: The results of this study show that our models can predict blood pressure over 4 weeks. Our models work for an individual with high variability of blood pressure. Therefore, we consider that our prediction models are valuable for blood pressure management.