Collision Computing for PPG Signal Analysis
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Solution Overview
Problem
Current noninvasive methods for measuring blood glucose levels, such as near-infrared spectroscopy, face challenges due to the low concentration of glucose in tissues and interference from other substances, leading to inaccurate results and regulatory approval issues.
Innovation Solution
The implementation of collision computing techniques, which involve nonlinear computational collisions between waveforms to enhance the detection and measurement of glucose by amplifying analyte-specific frequency components and separating them from clutter, using co-dependent waveforms to process sensor data and estimate the Net Analyte Signal (NAS).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If near-infrared spectroscopy is used to measure blood glucose noninvasively, then the measurement can be performed without drawing blood, but the accuracy deteriorates due to low glucose concentration and interference from other substances
Solution Approach 1:
The patent extracts the analyte-specific frequency components from the complex PPG signal by performing computational collisions between the measured signal and reference waveforms. This separation isolates the glucose-related information from interfering substances, enabling accurate noninvasive measurement despite the low concentration of glucose in tissue.
Solution Approach 2:
The patent introduces computational collision processing as an intermediary step between signal acquisition and glucose concentration calculation. The collision computing process acts as a mediator that transforms the raw PPG signal into enhanced analyte-specific components, bridging the gap between noninvasive measurement and accurate glucose detection.
2Quantity of substance
If conventional spectroscopic methods are used to detect glucose at low concentrations, then the measurement can be performed, but the results become inaccurate in high noise and clutter environments
Solution Approach 1:
The patent applies computational collision processing that vibrates or oscillates the signal processing in the frequency domain, allowing separation of analyte-specific frequency components from noise and clutter. This frequency-domain vibration enables detection of low-concentration glucose by emphasizing its characteristic frequency signature while suppressing background interference.
Solution Approach 2:
The patent changes the processing parameters by transforming the PPG signal from the time domain to the frequency domain through computational collision. This parameter transformation allows the system to detect glucose at low concentrations by operating in a domain where analyte-specific features are enhanced and noise is suppressed.
3Duration of action of moving object
If photoplethysmographic signals are used for heart rate measurement, then continuous monitoring is achieved, but accuracy deteriorates due to motion artifacts and environmental interference
Solution Approach 1:
The patent extracts heart rate information from the PPG signal by performing computational collisions that isolate the cardiac-related frequency components from motion artifacts and environmental interference. This extraction process maintains continuous monitoring capability while improving accuracy by separating the desired signal from contaminants.
Solution Approach 2:
The patent employs feedback mechanisms where the collision computing process continuously refines the separation of cardiac signals from artifacts. The system uses the enhanced analyte-specific components as feedback to improve ongoing heart rate measurements, maintaining accuracy during continuous monitoring despite varying motion conditions.
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 precise and accurate detection and measurement of glucose levels, even in high noise environments, by improving the signal-to-clutter ratio and providing reliable concentration estimates.
Implementation Method 1
a photodetector to generate a PPG signal
Implementation Method 2
measuring heart rate and other heart-related characteristics from photoplethysmographic (PPG) signals
Data Source
AI summary
In a noninvasive system for measurement of heart rate and other heart-related characteristics a photoplethysmogram (PPG) obtained from a tissue is divided into several feature waveforms, each corresponding to a PPG window of a particular length. Conditioned features, containing frequency components specific to heart-related events, are derived from the features by modulating a carrier kernel with such features. The conditioned features are computationally collided with one or more Zyotons that are co-dependent with the conditioned features. For each conditioned feature, one or more collisions selectively amplify frequency components in features sourced from PPG, and respective energy change values are obtained from such amplified energy portions. The resulting energy change values are analyzed to determine a smallest time-window likely containing heart rate and other heart-related events in the PPG data stream. Over time, the detected events are grouped and analyzed to determine heart rate and other heart-related characteristics.