A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

Abstract

Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night–day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP—estimated BP) of systolic BP was −0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was −0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.

Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring

“…Our team previously pretrained the expGPR model using a BP database of PPG signals measured at the wrist [ 30 ]. However, we measured the PPG signal sampled at a 256 Hz sampling rate at the upper arm in this study.…”

“…To compare the quality of the PPG signals obtained from the upper-arm and wrist-type BPMs, the BP values of all 30 participants at week 1 were measured by using the upper-arm, cuffless PPG BPM and the wrist-type PPG BPM [ 30 ]. The chronology was randomized.…”

“…The exponential Gaussian process regression (expGPR) has proven to be the most suitable machine-learning (ML) model for PPG physiological data [ 30 ]. The upper-arm, cuffless PPG BPM developed in this research adopts the expGPR model architecture with the fine-tuning method [ 31 , 32 ].…”

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

“…It was mentioned that the short-term validity of linear PTT models and the calibration need to be updated frequently under various conditions. The authors of [ 248 ] proposed a nonlinear ML-GPR model to estimate the regression between BP values and PPG features by grouping the age range of a user. They pointed out that calibrations can be implemented in an embedded system for personalized measurement and be adapted to different environments and health statuses.…”

A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation

“…The data-driven modeling method is a type of mathematical modeling, which uses process monitoring data to optimize the parameters in the mathematical model, in order to obtain a more accurate relation model. It has been widely used in complex physical processes and systems, such as intelligent detection [ 7 ], materials [ 8 ], medical health [ 9 ], etc. Generally, there are two types of data-driven modeling methods: one type includes artificial intelligence methods [ 10 ], such as neural networks, fuzzy algorithms, etc.…”