Respiration Rate Extraction from Photoplethysmograph Signals
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Solution Overview
Problem
Current patient monitoring systems face challenges in accurately extracting respiratory information from photoplethysmograph signals due to the complexity of signal morphology and variability, which affects the precision of respiration rate determination.
Innovation Solution
A patient monitoring system is configured to determine respiratory information by identifying reference points in the photoplethysmograph signal, calculating fiducial points based on these references, and generating a fiducial signal to derive morphology metrics such as down metrics, kurtosis, and delta of second derivatives, followed by autocorrelation and wavelet transforms to extract respiration rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to extract respiratory information from PPG signals, then the process is simpler, but the accuracy and reliability of respiration rate determination deteriorates due to signal morphology complexity and variability
Solution Approach 1:
The patent segments the PPG signal processing into distinct stages: detecting reference points (pulse wave maxima), calculating fiducial points using mathematical operations (first and second derivatives, time interval calculations), and then extracting respiratory information from the fiducial signal. This segmentation allows each stage to be optimized independently, improving measurement precision while managing complexity through structured processing steps.
Solution Approach 2:
The patent introduces fiducial points as an intermediary representation between the raw PPG signal and the final respiratory information extraction. By computing fiducial points based on reference points and specific time intervals (e.g., 210 milliseconds from pulse maxima), the system creates a simplified intermediate signal that preserves respiratory information while filtering out noise and morphological variability, thereby improving accuracy without requiring direct complex analysis of the raw signal.
2Measurement precision
If fiducial points are calculated based on reference points and time intervals, then the respiration rate accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary calculations of fiducial points based on reference points and predetermined time intervals before respiratory analysis. By pre-calculating these fiducial markers (such as points 210ms from pulse maxima) and storing them as an intermediate fiducial signal, the system prepares the data in advance for respiratory extraction, reducing real-time processing requirements while maintaining high accuracy.
3Reliability
If complex mathematical calculations are performed on the PPG signal to find reference and fiducial points, then the reliability of respiration information extraction improves, but the computational resources required increase
Solution Approach 1:
The patent extracts only the essential features needed for respiratory analysis by identifying reference points (pulse maxima) and calculating specific fiducial points based on predetermined time intervals from these references. Rather than performing continuous complex mathematical operations on the entire PPG signal, the system extracts discrete key points and uses these to create a simplified fiducial signal, thereby maintaining reliability while reducing computational energy consumption.
Data Source
AI summary
A signal representing physiological information may include information related to respiration. A patient monitoring system may utilize a wavelet transform to generate a scalogram from the signal. A threshold for the scalogram may be calculated, and scalogram values may be compared to the threshold. One of the scales meeting the threshold may be selected as representing respiration information such as respiration rate. The respiration information may be determined based on the selected scale.