Optical Blood Pressure Estimation Using P1-P2 Waveform Features
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Solution Overview
Problem
Existing non-invasive blood pressure measurement techniques fail to consider important features such as the systolic peak (P1) and pre-dicrotic peak (P2) in near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and speckle contrast optical spectroscopy (SCOS) data for accurate blood pressure estimation.
Innovation Solution
Utilizing optical patient monitoring systems that incorporate near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and speckle contrast optical spectroscopy (SCOS) to measure blood volume and flow changes, combined with machine learning algorithms, to estimate blood pressure by analyzing features like the P2/P1 ratio and heart rate, and generating a report indicative of estimated blood pressure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional non-invasive optical techniques (NIRS, DCS, SCOS) are used to measure blood pressure, then the measurement is non-invasive and continuous, but the accuracy is insufficient because important waveform features (P1, P2 peaks) are not considered
Solution Approach 1:
The patent segments the optical waveform into distinct components (P1 systolic peak, P2 pre-dicrotic peak, P3 dicrotic peak) and analyzes each segment separately. By dividing the continuous waveform into meaningful segments with specific features, the system can extract more information from each portion, thereby improving blood pressure estimation accuracy while utilizing previously overlooked waveform characteristics.
Solution Approach 2:
The patent transitions from traditional single-dimensional blood pressure measurement to multi-dimensional analysis by incorporating multiple waveform features (amplitude ratios, time intervals, derivative characteristics) across different dimensions. This dimensional expansion allows the system to capture complex hemodynamic information that correlates more accurately with blood pressure variations.
2Measurement precision
If multiple optical techniques (NIRS, DCS, SCOS) are combined to improve measurement accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent merges multiple optical measurement techniques (NIRS, DCS, SCOS) into a unified analysis framework that processes their combined output signals. By integrating the strengths of each technique and analyzing their composite waveform features together, the system achieves improved measurement accuracy while managing device complexity through unified signal processing algorithms.
Solution Approach 2:
The patent creates a universal blood pressure estimation system that can process and analyze data from multiple different optical techniques (NIRS, DCS, SCOS) using the same analytical framework. This multi-functional approach allows the system to accommodate various measurement modalities without requiring separate analysis pipelines for each technique, thereby improving accuracy while controlling complexity.
3Measurement precision
If traditional PPG waveform analysis is used without considering P1 and P2 peaks, then the analysis is simple, but the blood pressure estimation accuracy is insufficient
Solution Approach 1:
The patent applies preliminary signal processing steps (filtering, baseline correction, peak detection) to prepare the PPG waveform before analysis. By performing these preparatory actions first, the system identifies and marks the P1 and P2 peaks in advance, making subsequent blood pressure estimation more accurate while organizing the processing complexity into manageable sequential stages.
Solution Approach 2:
The patent replaces traditional mechanical or manual waveform analysis methods with automated computational algorithms that can systematically identify P1 and P2 peaks and calculate their relationships. This substitution of mechanical analysis with computational processing enables complex feature extraction and multi-parameter analysis without proportionally increasing operational complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately estimates systolic, diastolic, and mean arterial pressures using non-invasive methods, achieving high accuracy through multivariate regression and machine learning techniques.
Implementation Method 1
Near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and speckle contrast optical spectroscopy (SCOS) are non-invasive, diffuse optical techniques that can measure changes in blood volume and blood flow
Implementation Method 2
changes in blood flow can be measured by the quantification of the changes in the detected speckle pattern. By monitoring either changes in the temporal autocorrelation of the detected intensity at the microsecond to millisecond timescale (DCS)
Implementation Method 3
or the spatial blurring of the detected speckle pattern (SCOS), both methods relay information about the motion of cells in the vasculature
Implementation Method 4
The changes in blood volume modulate the measured light intensity (NIRS), and the pulsatile component of the optical signal is termed photoplethysmography (PPG)
Implementation Method 5
The pulsatile component of the blood flow signal is termed speckleplethysmography (SPG)
Data Source
AI summary
Optical patient monitoring systems are disclosed. The system may comprise an optical coupling system configured to transmit to and receive light signals from one or more locations on a subject; an optical processing system configured to generate optical data using the received light signals; and a computer programmed to receive the optical data; determine, using the optical data, at least one indicator of blood pressure; estimate an estimated blood pressure using the at least one indicator of blood pressure; and generate a report indicative of the estimated blood pressure. The at least one indicator of blood pressure comprises one or more of near-infrared spectroscopy (NIRS) data; photoplethysmography (PPG) data, diffuse correlation spectroscopy (DCS) data, speckle contrast optical spectroscopy (SCOS) data, speckleplethysmography (SPG) data, first derivative PPG data, second derivative PPG data, first derivative SPG data, second derivative SPG data, inflow (Fin) data, outflow (Fout) data, heart rate data, physiological data, and combinations thereof. Methods for estimating blood pressure are also disclosed.