Pulse Rate Determination via Correlation Sequence Analysis
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Solution Overview
Problem
Existing physiological monitoring systems face challenges in accurately determining pulse rate from photoplethysmographic signals due to noise components, 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 algorithm settings to qualify calculated values, applies signal conditioning techniques, and generates a correlation sequence to determine the pulse rate reliably, even in noisy conditions, by managing status flags and adjusting algorithm settings based on noise metrics and signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple operating modes and qualification techniques are used to improve pulse rate determination accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system dynamically switches between multiple operating modes (initialization mode, first mode with strict criteria, second mode with band-pass filtering) based on signal quality and noise conditions. The processing equipment adaptively adjusts algorithm settings and qualification techniques in real-time to optimize pulse rate determination accuracy while managing computational complexity through conditional execution rather than simultaneous processing of all modes.
2Measurement precision
If band-pass filtering is applied to reject noise, then measurement precision is improved, but the filter may be tuned to noise and deviate from correct physiological rate
Solution Approach 1:
The system employs qualification techniques that continuously monitor the filtered signal and provide feedback to verify whether the band-pass filter is correctly tuned to physiological rates. The processing equipment assesses whether calculated parameters are indicative of true physiological signals, and if the filter deviates to noise, the system detects this through qualification failure and adjusts or resets the filter settings to return to the correct physiological rate.
3Measurement precision
If strict criteria are used to determine physiological parameter, then measurement precision is improved, but productivity decreases due to more frequent mode drops
Solution Approach 1:
The system performs preliminary assessment of signal quality and noise characteristics before applying strict qualification criteria. By pre-evaluating signal conditions and selecting appropriate operating modes in advance, the system avoids unnecessary mode drops and recalibrations, thereby maintaining high measurement precision while improving overall throughput by preparing the processing pipeline proactively rather than reactively.
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 reliable determination of pulse rate by effectively filtering noise and adapting to varying signal conditions, ensuring accurate physiological parameter measurement even in challenging scenarios.
Implementation Method 1
a sensor, having a detector, which may generate an intensity signal (e.g., a PPG signal) based on light attenuated by the subject
Data Source
AI summary
A physiological monitoring system may determine physiological information, such as physiological rate information, from a physiological signal. The system may generate a correlation sequence between two segments of the physiological signal at multiple correlation lag values. The system may compare the correlation sequence to a predetermined threshold, which may vary as a function of lag. Based on the comparison, the system may determine whether the correlation sequence value exceeds the threshold, and whether the correlation sequence value corresponds to a peak. The system may identify a lag value when the correlation sequence corresponding to the lag value exceeds the threshold and corresponds to a peak. The system may determine physiological rate information based on the identified lag value.