Nonlinear features of photoplethysmography signals for Non-invasive blood pressure estimation
Biomedical Signal Processing and Control, cilt.85, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 85
- Basım Tarihi: 2023
- Doi Numarası: 10.1016/j.bspc.2023.105067
- Dergi Adı: Biomedical Signal Processing and Control
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, INSPEC
- Anahtar Kelimeler: Photoplethysmography, Poincare Plot, Nonlinear Analysis, Gaussian Process Regression, Blood Pressure
- Atatürk Üniversitesi Adresli: Hayır
Özet
Objective: 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. Methods: 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. Results: 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 of mean absolute error (MAE), standard deviation (STD), and Pearson's correlation coefficient. According to the results, application of the Gaussian process regression (GPR) led to the best performance for both systolic and diastolic BP (0.79 ± 3.08 and 1.38 ± 4.53 mmHg, respectively). Satisfying the criteria for Advancement of Medical Instrumentation (AAMI), it was rated as Grade A for systolic and diastolic BP measurements under terms of British Hypertension Society (BHS) standards, which confirms the efficiency of the Poincaré-based features for BP estimation. Conclusion: The results justify the usefulness of the proposed Poincaré-based features as a powerful tool in BP estimation scenarios.