Pulse Wave Signal Analysis Using Normalized Second Derivatives
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Solution Overview
Problem
Single-spot pulse wave velocity measurements in pulse wave signals are prone to noise and outliers due to difficulties in detecting fiducial points, particularly in the second derivative of the photoplethysmographic signal, leading to unreliable blood pressure estimates.
Innovation Solution
A method involving averaging over multiple cardiac cycles to reduce noise in the pulse wave signal, using normalized second derivatives and polynomial fits to identify and smooth fiducial points, allowing for improved analysis of reflected pulse waves and blood pressure estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If single-spot pulse wave velocity measurements are used to obtain blood pressure estimates, then the measurement process is simplified and less invasive, but the measurements become prone to noise and outliers due to difficulties in detecting fiducial points
Solution Approach 1:
The patent applies periodic action by averaging measurements across multiple cardiac cycles. The system identifies fiducial points in each cardiac cycle, computes pulse wave velocity for each cycle, then averages these velocities to produce a final blood pressure estimate. This periodic repetition and averaging reduces the impact of noise and outliers in individual cycles, resolving the contradiction between measurement simplicity and reliability.
2Measurement precision
If fiducial points are detected in the second derivative of the photoplethysmographic signal, then pulse wave velocity can be calculated, but noise and outliers increase due to detection difficulties
Solution Approach 1:
The patent applies preliminary action by performing several preparatory steps before final blood pressure calculation: (1) detecting fiducial points in each cardiac cycle, (2) filtering out outliers using statistical methods, (3) computing pulse wave velocity for each valid cycle, and (4) averaging the velocities. This preliminary processing of the signal and data reduces noise and detection difficulties, enabling more precise pulse wave velocity measurements.
Solution Approach 2:
The system uses feedback by comparing detected fiducial points across multiple cardiac cycles and using statistical analysis to identify and remove outliers. The average pulse wave velocity computed from multiple cycles provides feedback that improves the reliability of the final blood pressure estimate, compensating for detection difficulties in individual cycles.
3Reliability
If averaging over multiple cardiac cycles is performed, then noise is reduced and measurement robustness improves, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies partial action by averaging over a limited number of cardiac cycles (typically 5-10 cycles) rather than all available data. This provides sufficient noise reduction for robust blood pressure estimation while limiting the processing time to a practical duration, resolving the contradiction between measurement robustness and processing time.
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
Enhances the robustness of blood pressure estimation by reducing noise and improving the detection of fiducial points, enabling accurate analysis of cardiac cycle waveforms even under time-varying conditions.
Implementation Method 1
one or more photoplethysmography (PPG) sensors can be placed on a part of the body to obtain one or more PPG signals that represent the changes in volume of the blood flow
Data Source
Figure 1(a)~1(c)
Figure 2(a)~2(b)
Figure 3~4
AI summary
According to an aspect, there is provided a computer-implemented method for analysing a pulse wave signal, PWS, obtained from a subject. The PWS comprises pulse wave measurements for a plurality of cardiac cycles of the subject during a first time period. The method comprises (i) analysing (40) the PWS to identify a plurality of cardiac cycles and a respective reference point for each identified cardiac cycle; (ii) determining 2PWS as a second derivative with respect to time of the PWS; (iii) determining a normalised 2PWS by, for each part of the 2PWS corresponding to a respective identified cardiac cycle, normalising said part of the 2PWS with respect to the amplitude of the 2PWS at the identified reference point for said cardiac cycle; (iv) for a first lag time value, determining (42) an n-th order polynomial fit for a first set of values of the normalised 2PWS, wherein the first set of values of the normalised 2PWS comprises the values of the normalised 2PWS occurring the first lag time value from the reference point of each identified cardiac cycle, wherein n is equal to or greater than 1; (v) performing (44) one or more further iterations of step (iv) for one or more further lag time values to determine respective further n-th order polynomial fits for respective sets of values of the normalised 2PWS, wherein a respective set of values of the normalised 2PWS comprises the values of the normalised 2PWS that occur the respective further lag time value from the reference point of each identified cardiac cycle; and (vi) forming (46) a first average cardiac cycle waveform for a first time point in the first time period, wherein the first average cardiac cycle waveform is formed from values of the plurality of n-th order polynomial fits at the first time point.