Pulse Oximeter Motion Artifact Removal via Histogram Mode Analysis
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Solution Overview
Problem
Current pulse oximetry techniques face challenges in accurately measuring arterial oxygen saturation (SaO2) due to mechanical disturbances like body motion, which cause delays and smoothing of detection, and fail to quickly identify significant changes in SaO2, especially when body movement is vigorous.
Innovation Solution
A pulse oximeter that uses time-segmented data from multiple wavelengths to calculate slope values of regression lines, constructs histograms, and determines the mode value to eliminate artifacts from body motion, allowing for precise and rapid measurement of SaO2 changes without relying on peak and bottom determination of pulsative waveforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical techniques are used to estimate correct SaO2 values from preceding and subsequent data, then measurement reliability is improved, but time delay increases and detection speed deteriorates
Solution Approach 1:
The patent segments the measured light signal into multiple wavelength components and processes each segment independently through histogram analysis, allowing parallel computation that reduces overall processing time while maintaining measurement reliability through multiple data points
2Reliability
If statistical techniques are used to estimate correct SaO2 values, then measurement reliability is improved, but response speed deteriorates due to smoothing of SaO2 changes
Solution Approach 1:
The patent dynamically adjusts the histogram analysis based on the distribution of calculated SaO2 values, allowing the system to respond quickly to significant changes while maintaining reliability through statistical validation of the results
3Ease of operation
If peak and bottom determination of pulsative waveform is used, then measurement simplicity is maintained, but measurement precision deteriorates during vigorous body movement
Solution Approach 1:
The patent extracts the pulsative component from the total light signal by analyzing multiple wavelengths and using histogram distribution, effectively separating the arterial blood signal from motion artifacts without requiring complex peak detection algorithms
4Ease of manufacture
If conversion tables are used for two-wavelength apparatus, then ease of manufacture is improved, but adaptability deteriorates when more wavelengths are used
Solution Approach 1:
The patent changes the fundamental parameter from using fixed conversion tables to using histogram-based statistical distribution, which allows the system to adapt to any number of wavelengths while maintaining ease of manufacture through standardized processing algorithms
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 effectively eliminates artifacts from body motion, enhances measurement accuracy, and enables timely detection of SaO2 changes, ensuring accurate and rapid assessment of oxygen saturation even during vigorous body movement.
Implementation Method 1
a light receiving portion which receives the light beams that are emitted from the light emitting portion, and that are transmitted through or reflected from the living tissue, and which converts the light beams to electric signals
Implementation Method 2
an optical-density-variation calculating portion which obtains optical density variations for the living tissue on the basis of variations of the transmitted or reflected light beams of different wavelengths
Data Source
AI summary
A pulse oximetry includes: irradiating living tissue with a plurality of light beams of different wavelengths; receiving the light beams transmitted through or reflected from the living tissue and converting the received light beams to electric signals which correspond to the different wavelengths; time-segmenting time series data of the electric signals; calculating, with respect to each of the segmented time series data of the electric signals, a slope value of a regression line between each two of the electric signals; calculating SaO2 based on the slope value of each of the segmented time series data of the electric signals; constructing a histogram of SaO2 for each predetermined number of time segments; and obtaining a mode value from the histogram as SpO2 to be output of the pulse oximetry.