Physiological Signal Correlation Lag Qualification
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Solution Overview
Problem
Existing physiological monitoring systems face challenges in accurately determining pulse rates from photoplethysmographic signals due to noise, subject movement, and variations in pulse shape, particularly in subjects with low perfusion or dicrotic notches, which can lead to incorrect rate determination.
Innovation Solution
The system employs a processing module that uses multiple operating modes, band-pass filtering, and skew metric analysis to qualify correlation lag values, adjusting algorithm settings based on noise metrics and signal conditioning techniques to filter out noise and accurately determine pulse rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If band-pass filtering is applied to reject noise, then noise rejection is improved, but the system may be initially tuned to noise and deviate from the correct physiological rate
Solution Approach 1:
The patent implements a feedback mechanism where the correlation lag qualification results are used to adjust the band-pass filter tuning. The system continuously monitors whether the correlation lag values are qualified and uses this information to refine the filter settings, ensuring that noise rejection does not lead to incorrect rate determination. This closed-loop approach allows the system to learn from its performance and adaptively improve both noise rejection and accuracy.
Solution Approach 2:
The system dynamically changes the band-pass filter parameters (center frequency, bandwidth) based on the qualification of correlation lag values. When noise is detected or qualification fails, the system adjusts these parameters to optimize performance. This parameter adaptation allows the filter to maintain effectiveness across varying physiological conditions while avoiding being trapped in noise-induced states.
2Reliability
If multiple operating modes with different criteria are used to determine physiological parameters, then measurement reliability is improved, but device complexity increases
Solution Approach 1:
The patent divides the physiological parameter determination process into multiple operating modes, each handling specific scenarios or criteria. By segmenting the overall task into manageable modes with specialized algorithms, the system achieves high reliability for each specific condition while keeping individual mode complexities low. The qualification process itself is segmented into distinct steps that can be independently evaluated.
Solution Approach 2:
The patent creates a universal framework that accommodates multiple operating modes through a common qualification mechanism. The correlation lag qualification process serves as a universal validator that works across different modes and criteria, providing a unified approach to ensuring reliability regardless of which specific mode is active. This multi-functional design allows the system to handle diverse physiological conditions with a single robust framework.
3Measurement precision
If correlation calculation is performed to determine pulse rate, then pulse rate determination is achieved, but the system may be affected by noise components and incorrect rate determination
Solution Approach 1:
The patent introduces correlation lag qualification as an intermediary step between the correlation calculation and the final pulse rate determination. This intermediate validation process acts as a mediator that filters out incorrect correlation results caused by noise, ensuring that only qualified correlation lags are used to determine the pulse rate. The qualification mechanism protects the final measurement from noise-induced errors.
Solution Approach 2:
The system performs preliminary qualification of correlation lag values before using them for pulse rate determination. By pre-validating the correlation results against established criteria and checking for noise contamination, the system ensures that only reliable correlation data proceeds to the rate calculation stage. This preliminary filtering action prevents noise from corrupting the final measurement.
Data Source
AI summary
A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may determine a skew metric based on the physiological signal. The system may also determine a correlation lag value corresponding to a peak in a correlation sequence derived from the physiological signal. The system may qualify or disqualify the correlation lag value based on the skew metric. The system may, for example, compare the skew metric and the correlation lag value to a reference set of skew metric values and correlation lag values to determine whether to qualify or disqualify the correlation lag value.