Development of Predictive Remote Photoplethysmography Algorithm for Blood Pressure Assessment and Monitoring
Development of Predictive Remote Photoplethysmography Algorithm for Blood Pressure Assessment and Monitoring
Significant advancements in the field of medical technologies have resulted in the rise of contact-free methods of haemodynamic monitoring. Remote photoplethysmography (rPPG) is a videobased, contactless form of monitoring that operates through a camera-enabled device. This innovation interprets minute variations in skin colour due to blood flow which, when analysed with complex signal processing algorithms, generates vital sign readings. Currently, Nervotec's rPPG technology allows for the collection of rPPG waveforms, which enables the measurement of heart rate, heart rate variability, respiration rate and blood oxygen saturation (SpO2) level through signal processing techniques. The plethysmography signals can be used to estimate blood pressure through the creation and training of a predictive model. By examining and extracting key features of a continuous PPG waveform by training an artificial neural network, correlations between these features and BP can be studied.
This is a prospective, feasibility study.Background literature has shown that plethysmography (PPG) signals can be used to estimate blood pressure through the creation and training of a predictive model. By examining and extracting key features of a continuous PPG waveform by training an artificial neural network, correlations between these features and blood pressure can be studied.
Nervotec, a digital health and AI company , has successfully used rPPG technology to allow for collection of rPPG waveforms, which enables the measurement of heart rate (HR), heart rate variability (HRV), respiration rate (RR) and blood oxygen saturation (SpO2) level through signal processing techniques. They have incorporated rPPG technology into a mobile application using smartphone cameras for scanning an individual's face. However, this is yet to be established for blood pressure. We aim to perform a prospective study to evaluate the feasibility and accuracy of obtaining blood pressure measurements through rPPG.
Hypothesis: Remote photoplethysmography can be used to determine blood pressure and this algorithm can develop into a customised smartphone based application.
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