Blood Pressure Prediction Based on Single Photoplethysmography

Using real-time data to measure blood pressure from single photoplethysmography (PPG) is proposed. Due to an increase in publicly available datasets, the application of machine learning techniques in medical research studies has expanded in recent years. The Datasets utilized in this work were taken from the Queensland Vital Signs Dataset. Five feature vectors from the Photoplethysmography (PPG) signal are extracted and sampled at a rate of 25Hz. The R2 score value and Mean Square Error (MSE) are used to measure the performance of the various models. The best results for Systolic Blood Pressure are 0.91 and 7.76, respectively. While for Diastolic Blood Pressure the best results achieved were 0.87 and 7.06, respectively.