Pulse Oximetry Image Reconstruction for Localized SpO2 Errors
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Solution Overview
Problem
Pulse oximetry measurements can be inaccurate due to spatially localized measurement inconsistencies, particularly at certain body areas like the wrist, leading to incorrect estimates of physiological characteristics such as SpO2, despite underlying photoplethysmogram signals appearing valid.
Innovation Solution
An algorithm is employed to detect and mitigate spatially localized measurement inconsistencies by processing signals from multiple channels and wavelengths, using a measurement inconsistency mitigation algorithm to improve estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pulse oximetry systems use light emitters and light detectors to measure physiological signals, then measurements can be obtained, but measurement accuracy deteriorates when light emitters or detectors are not in good contact with tissue or when anomalies exist in the light path
Solution Approach 1:
The system divides the measurement into multiple spatial channels (first channel, second channel, third channel) with different emitter-detector configurations. Each channel provides an independent measurement path, allowing the system to segment the overall measurement task and identify which specific channel is affected by poor contact or anomalies, thereby maintaining accuracy in other channels.
Solution Approach 2:
The patent applies local quality by treating each measurement channel differently based on its specific characteristics and quality metrics. The system calculates quality metrics for each channel individually and uses this information to determine whether to trust measurements from specific channels, applying different weights or processing methods to different local measurement regions.
2Adaptability or versatility
If the system uses multiple light wavelengths to estimate physiological characteristics, then measurement coverage is improved, but the complexity of signal processing and anomaly detection increases
Solution Approach 1:
The system segments the measurement task by processing each wavelength separately through dedicated quality metric calculations and anomaly detection algorithms. This allows the complex multi-wavelength processing to be broken down into manageable separate operations, improving versatility while controlling complexity through modular processing.
Solution Approach 2:
The patent introduces quality metrics as intermediary variables that mediate between the raw multi-wavelength measurements and the final physiological estimates. These quality metrics serve as intermediaries to assess measurement reliability and guide the selection or weighting of different wavelength measurements, simplifying the overall processing complexity.
3Reliability
If the system processes signals from multiple channels and wavelengths to improve accuracy, then measurement robustness is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by calculating quality metrics for each channel and wavelength combination before final physiological estimation. This preliminary quality assessment allows the system to pre-identify problematic measurements and prepare appropriate processing strategies in advance, reducing overall processing time while maintaining robustness.
Solution Approach 2:
The patent applies partial action by selectively processing only the necessary channels and wavelengths based on quality metrics. Instead of uniformly processing all measurements, the system applies enhanced processing only where needed (e.g., when quality metrics indicate potential anomalies), reducing computational time while maintaining measurement robustness through targeted processing.
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
The algorithm enhances the accuracy of SpO2 estimation by identifying and addressing measurement inconsistencies, ensuring more reliable physiological signal measurements.
Implementation Method 1
utilize one or more light emitters to illuminate a user's tissue and one or more light detectors to receive light that enters and probes a subsurface volume of tissue
Implementation Method 2
an algorithm may be used to detect spatially localized measurement inconsistency (e.g., inconsistency above a threshold) and to mitigate or reduce its effect
Data Source
AI summary
A characteristic (e.g., SpO2) of a user's physiological signals can be estimated using a pulse oximeter. In some examples, inconsistent measurement of the physiological characteristic may occur despite the underlying physiological signals having quality characteristics consistent with physiologically valid signals showing a consistent cardiac signal indicative of accurate measurement of the physiological characteristic. In particular, the measurement inconsistency may be associated with a spatially localized region. Such measurement inconsistency may result in an incorrect, low estimate of the physiological characteristic relative to the true characteristic (e.g., the SpO2 estimate may skew lower than the true SpO2). An algorithm may be used to detect spatially localized measurement inconsistency and to mitigate or reduce its effect to improve the accuracy of the estimate of the physiological characteristic.