Machine learning and deep learning for blood pressure prediction: a methodological review from multiple perspectives

Blood pressure (BP) estimation is one of the most popular and long-standing topics in health-care monitoring area. The utilization of machine learning (ML) and deep learning (DL) for BP prediction has made remarkable progress recently along with the development of ML and especially DL technologies, and the release of large-scale available datasets. In this survey, we present a comprehensive, systematic review about the recent advance of ML and DL for BP prediction. To start with, we systematically sort out the current progress from four perspectives. Then, we summarized commonly-used datasets, evaluation metrics as well as evaluation procedures (especially the usually ignored splitting strategy operation), which is followed by a critical analysis about the reported results. Next, we discussed several practical issues as well as newly-emerging techniques appeared in the research community of BP prediction. Also, we introduced the potential application of several advanced ML technologies in BP estimation. Last, we discussed the question of what a good BP estimator should look like?, and then a general proposal for an objective evaluation of model performance is given from the perspective of an ML researcher. Through this survey, we wish to provide a comprehensive, systematic, up-to-date (to Feb, 2022) review of related research on BP prediction using ML & DL methods, which may be helpful to researchers in this area. We also appeal an objective view of the progress reported in the relevant literatures in a more systematic manner. The experimental data & code and other useful resources are available at https://github.com/v3551G/BP-prediction-survey.