WAKE-BPAT: Wavelet-Based Adaptive Kalman Filtering for Blood Pressure Estimation Via Fusion of Pulse Arrival Times

The paper is motivated by recent urgency to design continuous and cuff-less blood pressure (BP) monitoring solutions to prevent, detect, and treat the hypertension. In this regard, we propose a novel wavelet-based feature extraction algorithm coupled with an adaptive and multiple-model Kalman filtering framework (referred to as the WAKE-BPAT), which provides accurate and dynamic BP estimates by extraction and fusion of different pulse arrival time (PAT) features. In particular, a wavelet transform and histogram analysis-based robust and high-accurate R-peak detection algorithm is proposed without incorporation of any pre-defined thresholds. This in combination with high-quality photoplethysmogram (PPG) characteristic points obtained from signal recordings of a recently developed PPG device (Gen-1), are used for BP estimation, which is modeled as a hybrid state-space model with structural uncertainties to fuse different PAT features in an adaptive fashion. Our experimental evaluations based on a real data set collected via Gen-1 device confirms the superiority of the proposed WAKE-BPAT framework in comparison to its counterparts.