Time-Segmented Pulse Oximetry for Motion Artifact Reduction
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Solution Overview
Problem
Conventional pulse oximetry struggles with accurately measuring arterial oxygen saturation (SaO2) due to disturbances from body motion, leading to errors in peak and bottom detection of pulse waveforms, and existing methods for correction are inadequate, especially during vigorous movements.
Innovation Solution
The method involves irradiating tissue with multiple light beams of different wavelengths, dividing electrical signals into time segments, calculating gradients of regression lines, and smoothing these values to obtain accurate arterial oxygen saturation, eliminating the need for precise peak and bottom detection and reducing artifacts from body motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pulse oximetry uses peak and bottom detection of pulse waveforms to measure SaO2, then the measurement process is simple, but measurement accuracy deteriorates due to body motion artifacts
Solution Approach 1:
The patent divides the measured light signal into multiple time segments and processes each segment separately to calculate SaO2 values. This segmentation allows the system to handle body motion artifacts by processing smaller, more manageable time intervals where motion effects are less dominant, thereby improving measurement accuracy without requiring overly complex real-time correction mechanisms.
Solution Approach 2:
The patent performs preliminary processing of the light signal by dividing it into time segments before calculating SaO2 values. This preliminary segmentation prepares the data for more accurate processing by establishing a structured time-based framework that facilitates subsequent gradient calculations and artifact reduction, improving measurement precision before the final SaO2 computation.
2Reliability
If statistical methods are used to estimate SaO2 from data before and after target points, then body motion artifacts are reduced, but detection delay increases
Solution Approach 1:
The patent segments the time series data into multiple intervals and calculates SaO2 for each segment independently. This approach provides frequent SaO2 updates without requiring extensive data from before and after the target point, thereby maintaining measurement reliability while reducing detection delay compared to conventional statistical methods that need broader temporal windows.
Solution Approach 2:
The patent uses a partial approach by calculating gradients over limited time segments rather than requiring extensive data before and after the target point. This partial action sufficient for reliable SaO2 estimation while minimizing detection delay, achieving a balance between measurement reliability and response time.
3Measurement precision
If base line correction is applied to measured waveforms, then some artifacts are reduced, but effectiveness is insufficient during vigorous body motions
Solution Approach 1:
The patent divides the waveform into multiple time segments and processes each segment separately to calculate SaO2. This segmentation approach is more adaptable to vigorous body motions than simple base line correction because it handles each time interval independently, allowing the system to accommodate varying motion conditions in different segments without requiring a single correction to work for all conditions.
Solution Approach 2:
The patent employs a dynamic approach by calculating gradients within each time segment rather than applying a static base line correction to the entire waveform. This dynamic processing adapts to changing motion conditions in each segment, providing better artifact reduction during vigorous body motions compared to fixed base line correction methods.
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
This approach enhances the accuracy of SaO2 measurement by utilizing time-series data and smoothing techniques, allowing for early detection of SaO2 changes and reducing errors caused by body motion, thereby improving measurement reliability.
Implementation Method 1
a light receiver which receives the light transmitted through the living tissue; a current-voltage converter which converts an output signal of the light receiver into a voltage signal
Data Source
AI summary
To measure oxygen saturation in blood, living tissue is irradiated with a first light beam having a first wavelength and a second light beam having a second wavelength. A first electrical signal is generated from the first light beam reflected from or transmitted through the tissue. A second electrical signal is generated from the second light beam reflected from or transmitted through the tissue. The first electrical signal is divided into a plurality of first segments, each including a part of the first electrical signal for a predetermined time period. The second electrical signal is divided into a plurality of second segments, each including a part of the second electrical signal for the predetermined time period. A gradient of a regression line is calculated between every one of the first segments and an associated one of the second segments, thereby obtaining a plurality of gradients.