Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?

Koshiro Kido; Zheng Chen; Ming Huang; Toshiyo Tamura; Wei Chen; Naoaki Ono; Masachika Takeuchi; Md Altaf-Ul-Amin; Shigehiko Kanaya

doi:10.3390/life12010011

Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?

Life (Basel). 2021 Dec 22;12(1):11. doi: 10.3390/life12010011.

Authors

Koshiro Kido¹, Zheng Chen¹, Ming Huang¹, Toshiyo Tamura², Wei Chen³, Naoaki Ono^{1

4}, Masachika Takeuchi⁵, Md Altaf-Ul-Amin¹, Shigehiko Kanaya^{1

4}

Affiliations

¹ Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan.
² Institute for Healthcare Robotics, Waseda University, Tokyo 162-0041, Japan.
³ Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 201203, China.
⁴ Data Science Center, Nara Institute of Science and Technology, Ikoma 630-0192, Japan.
⁵ San-Ei Medisys Co., Ltd., Kyoto 607-8116, Japan.

Abstract

Using the Plethysmograph (PPG) signal to estimate blood pressure (BP) is attractive given the convenience and possibility of continuous measurement. However, due to the personal differences and the insufficiency of data, the dilemma between the accuracy for a small dataset and the robustness as a general method remains. To this end, we scrutinized the whole pipeline from the feature selection to regression model construction based on a one-month experiment with 11 subjects. By constructing the explanatory features consisting of five general PPG waveform features that do not require the identification of dicrotic notch and diastolic peak and the heart rate, three regression models, which are partial least square, local weighted partial least square, and Gaussian Process model, were built to reflect the underlying assumption about the nature of the fitting problem. By comparing the regression models, it can be confirmed that an individual Gaussian Process model attains the best results with 5.1 mmHg and 4.6 mmHg mean absolute error for SBP and DBP and 6.2 mmHg and 5.4 mmHg standard deviation for SBP and DBP. Moreover, the results of the individual models are significantly better than the generalized model built with the data of all subjects.

Keywords: blood pressure; cuffless measurement; longitudinal experiment; nonlinear regression; plethysmograph.

Abstract

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