Evaluation of Cuff-less Blood Pressure Monitoring Models over Multiple Data Sets

Blood pressure (BP) monitoring via cuffless devices has gained significant attention in the last few years. Despite a plethora of works having been produced in this field based on traditional machine learning (ML) or deep learning (DL) models, very limited research has been carried out in terms of the external validation and reproducibility of said models to ensure that they are of clinical use. To the best of the authors' knowledge, this is the first study to evaluate several of the currently most well cited ML/DL-based models for cuffless BP monitoring over multiple independent data sets. The results of this investigation in reproducibility are reported with particular recommendations provided regarding standardized data collection protocols, models and signals, data recording length, and open access data as potential steps to overcoming the challenge of reproducibility in ML/DL models in this field and the health domain in general.