Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals

Abstract

Blood pressure measurements are one of the most routinely performed medical tests globally. Blood pressure is an important metric since it provides information that can be used to diagnose several vascular diseases. Conventional blood pressure measurement systems use cuff-based devices to measure the blood pressure, which may be uncomfortable and sometimes burdensome to the subjects. Therefore, in this study, we propose a cuffless blood pressure estimation model based on Monte Carlo simulation (MCS). We propose a heterogeneous finger model for the MCS at wavelengths of 905 nm and 940 nm. After recording the photon intensities from the MCS over a certain range of blood pressure values, the actual photoplethysmography (PPG) signals were used to estimate blood pressure. We used both publicly available and self-made datasets to evaluate the performance of the proposed model. In case of the publicly available dataset for transmission-type MCS, the mean absolute errors are 3.32 ± 6.03 mmHg for systolic blood pressure (SBP), 2.02 ± 2.64 mmHg for diastolic blood pressure (DBP), and 1.76 ± 2.8 mmHg for mean arterial pressure (MAP). The self-made dataset is used for both transmission- and reflection-type MCSs; its mean absolute errors are 2.54 ± 4.24 mmHg for SBP, 1.49 ± 2.82 mmHg for DBP, and 1.51 ± 2.41 mmHg for MAP in the transmission-type case as well as 3.35 ± 5.06 mmHg for SBP, 2.07 ± 2.83 mmHg for DBP, and 2.12 ± 2.83 mmHg for MAP in the reflection-type case. The estimated results of the SBP and DBP satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards and are within Grade A according to the British Hypertension Society (BHS) standards. These results show that the proposed model is efficient for estimating blood pressures using fingertip PPG signals.

Noninvasive blood oxygen, heartbeat rate, and blood pressure parameter monitoring by photoplethysmography signals

“…The model obtained after learning and training is used to predict the blood pressure of new subjects and to have continuous blood pressure values for comparison. Most of the previous reports also used the waveform in the MIMIC-II database as the object of analysis [ 22 , 23 , 24 ]. However, the biggest problem of deep learning is that a vast amount of data is required for training to avoid the overfitting phenomenon of the model to ensure that the trained model has extremely high accuracy for the training data, but there are substantial errors in the actual test data.…”

“…The theory of predicting blood pressure with the PPG signal alone is derived from the time difference relationship between the ECG signal and the PPG signal, similar to early derivations [ 9 ], and there is also a model that uses a neural network to extract the eigenvalues of the PPG signal for training directly [ 10 , 11 ]. Some theories are based on the original waveform of the time-domain PPG signal in addition to the first-order or even the second-order derivative waveform [ 12 ]. There is also a correlation between the amplitude and the wave height obtained from the complete waveform shape (such as the time value corresponding to the wave height percentage of the time percentage) [ 13 ].…”

A Comparative Analysis of Various Machine Learning Algorithms to Improve the Accuracy of HbA1c Estimation Using Wrist PPG Data

“…One promising approach is the use of wrist photoplethysmography (PPG) data, which measure the changes in blood volume in the microvascular bed of tissue via light-emitting diodes (LEDs) and a photodetector (PD). PPG data have previously been employed for various health-monitoring applications such as heart rate, SpO 2 , and blood pressure estimation [ 6 , 7 ]. In addition, recent studies on glucose or HbA1c estimation using PPG signals have been published [ 8 , 9 , 10 , 11 , 12 , 13 ].…”

Photoplethysmography-Based Distance Estimation for True Wireless Stereo

“…PPG sensors emit infrared rays to the skin and measure the amount of blood flow by determining the amount of rays absorbed in red blood cells. Because the PPG data are affected by heart rate due to this operation method, PPG sensors provide healthcare services such as heart rate measurement, breathing rate estimation, atrial fibrillation, and blood pressure measurement [ 7 , 8 , 9 , 10 ]. In addition, since heart rate has specific patterns, applying the deep learning for pattern recognition on PPG signal was researched [ 11 , 12 ].…”