WAKE-BPAT: Wavelet-Based Adaptive Kalman Filtering for Blood Pressure Estimation Via Fusion of Pulse Arrival Times
Golnar Kalantar, Sourav Kumar Mukhopadhyay, Fatemeh Marefat, Pedram Mohseni, Arash Mohammadi
Abstract
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 <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">different pulse arrival time</i> (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.
BibTeX
@inproceedings{icassp2018_wakebpatwaveletb,
title = {WAKE-BPAT: Wavelet-Based Adaptive Kalman Filtering for Blood Pressure Estimation Via Fusion of Pulse Arrival Times},
author = {Golnar Kalantar and Sourav Kumar Mukhopadhyay and Fatemeh Marefat and Pedram Mohseni and Arash Mohammadi},
booktitle = {ICASSP 2018},
year = {2018}
}