PPG-Based Systolic Blood Pressure Estimation Method Using PLS and Level-Crossing Feature

• Published in: Applied sciences Vol. 9; no. 2; p. 304
• Main Authors: Fujita, Daisuke, Suzuki, Arata, Ryu, Kazuteru
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 16.01.2019
• Subjects: Blood pressure; blood pressure estimation; Electrocardiography; Hypertension; Light emitting diodes; Methods; Multivariate analysis; Noise; partial least-squares regression; photoplethysmography; Regression analysis; Sensors; Trends; Waveforms; Japan
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9020304

This paper proposes a cuff-less systolic blood pressure (SBP) estimation method using partial least-squares (PLS) regression. Level-crossing features (LCFs) were used in this method, which were extracted from the contour lines arbitrarily drawn on the second-derivative photoplethysmography waveform. Unlike conventional height ratio features (HRFs), which are extracted on the basis of the peaks in the waveform, LCFs can be reliably extracted even if there are missing peaks in the waveform. However, the features extracted from adjacent contour lines show similar trends; thus, there is a strong correlation between the features, which leads to multicollinearity when conventional multiple regression analysis (MRA) is used. Hence, we developed a multivariate estimation method based on PLS regression to address this issue and estimate the SBP on the basis of the LCFs. Two-hundred-and-sixty-five subjects (95 males and 170 females [(Mean ± Standard Deviation) SBP: 133.1 ± 18.4 mmHg; age: 62.8 ± 16.8 years] participated in the experiments. Of the total number of subjects, 180 were considered as learning data, while 85 were considered as testing data. The values of the correlation coefficient between the measured and estimated values were found to be 0.78 for the proposed method (LCFs + PLS), 0.58 for comparison method 1 (HRFs + MRA), and 0.62 for comparison method 2 (HRFs + MRA). The proposed method was therefore found to demonstrate the highest accuracy among the three methods being compared.