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This paper presents a blood pressure estimation method based on pulse wave velocity (PWV). Although there are a variety of methods based on PWV to estimate blood pressure, most of them require calibration per patient, and the patient has to remain still. The goal of our research is to develop a calibration-free blood pressure estimation method that is applicable not only during rest but also during exercise. To accomplish our goal, we extracted properties of blood vessels from photoplethysmogram (PPG) signals, and compared several regression models, such as the deductive model based on blood vessel physics equation, and the inductive model based on machine learning. Twenty-four participants performed exercise, measuring blood pressure, electrocardiogram (ECG) and PPG. The best result showed that the mean error for the estimated systolic blood pressure (SBP) against cuff-based blood pressure was 0.18 ± 8.68 mmHg. Although there was not a big difference between the regression models, PWV and Augmentation Index are effective features to estimate SBP. In addition to this, Heart Rate was effective only for the young men, and height ratio of c-wave to a-wave of acceleration pulse wave might be effective for elderly men. These results suggest that our proposed method has the potential for cuff-less calibration-free blood pressure estimation which include measurements during rest and exercise.

Cuff-less Calibration-free Blood Pressure Estimation under Ambulatory Environment using Pulse Wave Velocity and Photoplethysmogram Signals