Physiological Monitor Algorithm Settings for Pulse Rate Accuracy
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Solution Overview
Problem
Determining physiological parameters such as pulse rate from photoplethysmographic signals is challenging due to noise components from ambient light, electromagnetic radiation, subject movement, and low perfusion, which can obscure the desired physiological signals.
Innovation Solution
A physiological monitor employs a processing module that uses various operating modes, filters, and algorithm settings to analyze intensity signals, apply band-pass filters, and perform correlation calculations to accurately determine pulse rate by qualifying calculated values and mitigating noise effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple operating modes and filters are used to improve pulse rate determination accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The processing module dynamically selects between multiple operating modes (first mode with strict criteria, second mode with band-pass filter) based on signal quality assessment. The system adapts its processing approach in real-time, switching modes to optimize pulse rate determination under varying noise conditions without requiring all processing paths to be active simultaneously, thus managing complexity while maintaining precision.
Solution Approach 2:
The processing module is divided into distinct operational segments or modes, each optimized for specific signal conditions. The first mode handles signals with strict qualification criteria, while the second mode employs band-pass filtering for noisy conditions. This segmentation allows the complex processing logic to be organized into manageable, condition-specific modules that can be selectively activated.
2Measurement precision
If band-pass filter 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 provide feedback on the accuracy of calculated physiological parameters. The qualification process assesses whether the band-pass filter has correctly identified the physiological rate by checking if the filter output corresponds to expected physiological patterns. If the qualification fails, the system adjusts the filter tuning or switches modes, ensuring the filter remains aligned with actual physiological signals rather than noise.
Solution Approach 2:
The system takes preliminary actions to prevent filter mis-tuning by implementing qualification checks before and during the filtering process. The first mode with strict criteria serves as a preliminary filter to identify valid signals before applying the band-pass filter in the second mode. This preliminary validation prevents the band-pass filter from being applied to noise-dominated signals, thereby preventing mis-tuning.
3Reliability
If strict criteria are used to determine physiological parameter, then reliability is improved, but productivity decreases due to more frequent mode drops
Solution Approach 1:
The system dynamically adjusts the strictness of determination criteria based on the operating mode and signal quality. In the first mode, strict criteria are applied to ensure high reliability when signals are clear. In the second mode, the band-pass filter relaxes some criteria by focusing on frequency-specific patterns, allowing continuous operation even in noisy conditions. This dynamic adjustment of criterion strictness maintains reliability while preventing excessive mode drops that would reduce productivity.
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
The solution reliably determines physiological parameters by effectively filtering noise and adjusting algorithm settings based on signal quality, ensuring accurate pulse rate measurement even in noisy conditions.
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
Implementation Method 2
Another mode may implement a relatively narrow, adjustable band-pass filter on the physiological data (e.g., time series data), which is good at rejecting noise when it is tuned to the correct rate
Implementation Method 3
Determining the value indicative of the physiological rate may include performing a correlation calculation such as a correlation
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 difference signal based on the physiological signal and sort the difference signal to generate a sorted difference signal. The system may identify a midpoint of a first segment of the difference signal and a midpoint of a second segment of the difference signal. The first segment may correspond to positive values of the difference signal and the second segment may correspond to negative values of the difference signal. The system may determine an algorithm setting based on the first midpoint and the second midpoint. The algorithm setting may, for example, affect the amount of filtering applied to the physiological signal.