SVR ensemble-based continuous blood pressure prediction using multi-channel photoplethysmogram

In this paper, a continuous non-occluding blood pressure (BP) prediction method is proposed using multiple photoplethysmogram (PPG) signals. In the new method, BP is predicted by a committee machine or ensemble learning framework comprising multiple support vector regression (SVR) machines. The existing methods for continuous BP prediction rely on a single calibration model obtained from a single arterial segment. Our ensemble framework is the first BP estimation method which uses multiple SVR models for calibration from multiple arterial segments. This permits reducing of the mean prediction error and the risk of overfitting associated with a single model. Each SVR in the ensemble is trained on a comprehensive feature set that is constructed from a distinct PPG segment. The feature set includes pulse morphological parameters such as systolic pulse amplitude and area under the curve, heart rate variability (HRV) frequency, time domain parameters and the pulse wave velocity (PWV). Empirical evaluation using 40 volunteers with no serious health conditions shows that the proposed method is more reliable for estimating both the systolic and diastolic BP than similar methods employing a single calibration model under identical settings. Moreover, the combined output is found to be more stable than the output of any of the constituent models in the ensemble for both the systolic and diastolic cases.