Current Developments in Cuff-Free Non-invasive Continuous Blood Pressure Estimation Using Photoplethysmography

Continuous Blood Pressure monitoring is an important topic of research to improve healthcare sector worldwide. Complexity of blood pressure mechanism makes non-invasive acquisition task more difficult. Further, existing BP monitors are not user friendly, and trainer is needed for accurate BP estimation. Because of numerous drawbacks in existing blood pressure monitors, several authors suggest mathematical model using PPG and other physiological signal. Recent developments in Artificial Neural Network, machine learning, and deep learning algorithm increased the precision of predicted blood pressure. These methods are highly reliable for mobile health and wearable technology. Still, there are bunch of options to study in this domain and develop a robust wearable device to estimate continuous blood pressure as well as classify hypertension more precisely. The purpose of this work is to look at some of the most important studies, their approaches, existing device based on these approaches, the reliability of these devices, and suggested solutions for improving the findings.