Exponential Sampling Rate for Pulse Oximetry Signal Digitization
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Solution Overview
Problem
Pulse oximeters typically sample light intensity signals periodically, which may not capture the maximum amplitude effectively, leading to inaccurate digitization of physiological characteristics due to insufficient sampling density during the most significant portion of the pulse width period.
Innovation Solution
Implementing exponential sampling based on the waveform of the light intensity signal, increasing sampling frequency as the signal approaches maximum amplitude and reducing it during less relevant times, with options including oversampling at the Nyquist rate or sampling in proportion to signal amplitude.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If periodic sampling is used with even sampling density, then the sampling process is simple and uniform, but the maximum amplitude of the signal is not captured effectively
Solution Approach 1:
The patent applies dynamics by transitioning from static periodic sampling to dynamic exponential sampling. The sampling frequency is made variable and adaptive, increasing exponentially as the signal approaches maximum amplitude. This dynamic adjustment allows the system to capture critical signal features (maximum amplitude) with higher precision while maintaining simplicity through a mathematical sampling rule rather than complex control mechanisms.
Solution Approach 2:
The patent changes the sampling frequency parameter from constant to variable. By using exponential sampling, the frequency parameter evolves over time based on the signal characteristics. This parameter change enables the system to allocate more sampling points to critical regions (high amplitude portions) of the signal, improving measurement precision without requiring complex additional hardware.
2Measurement precision
If constant sampling frequency is used, then the processing is uniform and simple, but insufficient sampling density occurs during critical pulse amplitude periods
Solution Approach 1:
The system dynamically adjusts sampling frequency based on signal characteristics. During critical periods when the pulse amplitude is high, the sampling frequency increases exponentially to ensure sufficient density. During less critical periods, the sampling frequency decreases, maintaining processing efficiency. This dynamic approach resolves the contradiction by making the sampling rate adaptive rather than fixed.
Solution Approach 2:
The patent uses periodic action in the sense of repeated sampling cycles, but enhances it with variable frequency. The exponential sampling pattern creates a periodic sampling structure where the interval between samples varies systematically. This allows the system to maintain regular sampling rhythm while adjusting density according to signal importance, balancing precision and efficiency.
3Measurement precision
If higher sampling frequency is applied throughout the entire signal, then maximum amplitude is captured accurately, but processing power is wasted during less relevant times
Solution Approach 1:
The system dynamically modulates sampling frequency to match signal relevance. High sampling frequency is applied only when the signal is at maximum amplitude (most relevant period), and reduced frequency is applied during less relevant periods. This dynamic adaptation ensures accurate maximum amplitude capture when needed while conserving processing power during less critical times, resolving the energy efficiency contradiction.
Solution Approach 2:
The sampling frequency parameter is changed from constant high value to variable value that decreases over time. By using exponential sampling with decreasing frequency, the system maintains high precision for critical signal portions while reducing processing power consumption for less relevant portions. This parameter optimization balances measurement precision and energy efficiency.
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 digitized signals by ensuring higher sampling density at the most critical periods, improving the determination of physiological characteristics while conserving processing power by reducing sampling during less relevant times.
Implementation Method 1
a non-invasive sensor that transmits light through a patient's tissue and that photoelectrically detects the absorption and/or scattering of the transmitted light in such tissue
Implementation Method 2
Pulse oximeters typically utilize a non-invasive sensor that transmits light through a patient's tissue
Implementation Method 3
the light passed through the tissue is typically selected to be of one or more wavelengths that may be absorbed or scattered by the blood in an amount correlative to the amount of the blood constituent present in the blood
Implementation Method 4
a non-invasive sensor that transmits light through a patient's tissue and that photoelectrically detects the absorption and/or scattering of the transmitted light in such tissue
Data Source
AI summary
Methods and systems are provided that include sampling a light intensity signal at different frequencies based on the waveform of the signal to produce a more accurate digitized signal. The light intensity signal is an analog signal proportional to the intensity of light received at a detector of a pulse oximetry system. In one embodiment, the signal may be sampled exponentially during pulse width periods, such that the end of the pulse width periods where the signal reaches a maximum amplitude may be sampled more frequently. The signal may also be exponentially sampled or oversampled during periods when the signal is expected to near a maximum amplitude. Further, the signal may be sampled less frequently during low amplitude periods of the signal, and during dark periods, such that processing power may be conserved.