Nonlinear features of photoplethysmography signals for Non-invasive blood pressure estimation

• Published in: Biomedical signal processing and control 2023-08, Vol.85, p.105067, Article 105067
• Main Authors: Shoeibi, Fatemeh, Najafiaghdam, Esmaeil, Ebrahimi, Afshin
• Format: Article
• Language: English
• Subjects: Blood Pressure; Gaussian Process Regression; Nonlinear Analysis; Photoplethysmography; Poincaré Plot
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.105067

[Display omitted] •A record-by-record PPG approach is introduced for abnormal PPG signal elimination.•Poincaré section analysis of the PPG signals is proposed for feature extraction that has the advantage of releasing the requisites for precise detection of local points in the PPG signal.•A novel feature extraction benchmark based on Poincaré plot of the PPG signals is proposed that can reveal the relevant information and can improve the accuracy of BP estimation.•The newly proposed quantitative indices from three-time series, extracted from the Poincaré mapping of the PPG signal, can precisely describe the geometric appearance of the Poincaré plot, which in turn facilitates its application in the analysis of the nonlinear behavior of other physiological signals.•The Poincaré -based introduced features have a better performance in diastolic blood pressure estimation compared to systolic blood pressure estimation. Continuous monitoring of blood pressure (BP) plays an essential role in the prognosis and prevention of hypertension and related cardiovascular diseases. Moreover, the ever-increasing demand for portable continuous health monitoring systems coupled with promising capabilities of photoplethysmography (PPG) sensors for developing easy-to-use, portable wearable devices have motivated many researchers toward applying PPG signals for non-invasive health monitoring. Nonlinear nature of BP and proven capability of the Poincaré plot for analyzing dynamic behavior of nonlinear systems motivated us to use this powerful tool for BP estimation. This study aims to explore the dynamical behavior of PPG signals to assist feature extraction for machine-learning (ML) algorithms to estimate BP. We proposed a Poincaré-based feature extraction method for BP estimation with no need to extract precise local points on the PPG signal. First, a Poincaré plot of 10-s segments of the PPG signal was prepared. Then, three distinct time series were retrieved from Poincaré mapping of PPG signals; following this, numerous indices were extracted from the three time series. The F-Test feature selection method was applied to pick the most effective features. Finally, the picked features were fed into different ML algorithms for the estimation of BP. The proposed method was validated using a subset of the Medical Information Mart for Intensive Care II (MIMIC-II) database containing ambulatory blood pressure (ABP) and PPG records. The performance evaluation was carried out in terms o