Pulse Oximeter Using Photon Density Waves and Wavelet Analysis
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Solution Overview
Problem
Pulse oximetry devices do not fully utilize the information available from light transmission through tissue, leading to potentially useful data being uncollected and unused, limiting the information available to caregivers.
Innovation Solution
The use of photon density waves modulated at high frequencies to generate signals that include phase and amplitude information, processed using wavelet transforms to analyze physiological parameters such as total hemoglobin and oxygenated/deoxygenated hemoglobin ratios, allowing for the differentiation between physiological and non-physiological changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pulse oximetry devices use traditional light absorption analysis only, then the device complexity is low, but the measurement precision and information completeness are limited
Solution Approach 1:
The patent transitions from traditional light absorption analysis to photon density wave analysis by modulating light at high frequencies (e.g., 50-500 MHz). This adds a temporal frequency dimension to the measurement, enabling extraction of both absorption and scattering information from the phase and amplitude of the modulated light signal, thereby improving measurement precision without proportionally increasing device complexity
Solution Approach 2:
The patent changes the operating parameters of the light source by modulating it at high frequencies to generate photon density waves. This parameter change enables the detection system to measure both the amplitude (related to absorption) and phase (related to scattering) of the light signal, providing additional physiological information such as total hemoglobin concentration and oxygen saturation with improved accuracy
2Loss of information
If photon density waves with phase and amplitude information are used, then the information completeness improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent replaces traditional time-domain signal analysis with wavelet transform-based time-frequency analysis. This substitution enables effective processing of the complex phase and amplitude information from photon density waves, allowing extraction of physiological parameters while managing the analytical complexity through mathematical transformation rather than direct mechanical measurement
Solution Approach 2:
The patent uses periodic modulation of the light source at high frequencies to generate photon density waves. This periodic action creates a structured signal with well-defined phase and amplitude characteristics that can be systematically analyzed using wavelet transforms, making the detection and measurement process more tractable despite the increased information content
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 enables the extraction of additional physiological information beyond traditional pulse oximetry, improving the accuracy and completeness of blood oxygen saturation and other physiological parameter measurements by distinguishing between absorption and scattering changes.
Implementation Method 1
transmits light through a patient's tissue
Implementation Method 2
photoelectrically detects the absorption of the transmitted light in such tissue
Implementation Method 3
The use of photon density waves modulated at high frequencies to generate signals that include phase and amplitude information
Data Source
AI summary
Methods and systems are provided for transmitting and receiving photon density waves to and from tissue, and processing the received waves using wavelet transforms to identify non-physiological signal components and/or identify physiological conditions. A pulse oximeter may receive the photon density waves from the tissue to generate a signal having phase and amplitude information. A phase signal may be proportional to a scattering by total particles in the tissue, and an amplitude signal may correlate to an absorption by certain particles, providing information on a ratio of different particles in the tissue. Processing the phase and amplitude signals with wavelet transforms may enable an analysis of signals with respect to time, frequency, and magnitude, and may produce various physiological data. For example, non-physiological noise components may be identified, and certain physiological conditions may be identified by processing scalograms of the original signals with patterns corresponding to certain physiological conditions.