Physiological Signal Processing Module for Accurate Pulse Rate Determination
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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 qualification techniques to reject noise, adjust algorithm settings based on signal analysis, and manage status flags to ensure accurate pulse rate determination, even in noisy conditions, by applying filters and conditioning techniques such as finite impulse response filtering and signal normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If band-pass filtering is applied to reject noise, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The processing module is divided into multiple operating modes (first mode, second mode, third mode) that can be selectively activated based on signal conditions. Each mode implements different processing strategies appropriate for specific scenarios, allowing the system to achieve high measurement precision without always requiring the full complexity of all processing features.
Solution Approach 2:
The system dynamically adjusts its processing complexity by switching between different operating modes based on real-time signal quality assessment. The qualification techniques evaluate signal characteristics and activate appropriate filtering and processing levels, ensuring that band-pass filtering and other complex operations are applied only when necessary to achieve accurate pulse rate determination.
2Reliability
If multiple operating modes and qualification techniques are implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary signal quality assessment using qualification techniques before committing to specific processing paths. By evaluating signal characteristics in advance, the system can determine which operating mode is appropriate, ensuring reliable pulse rate determination while avoiding unnecessary complex processing for signals that don't require it.
Solution Approach 2:
The qualification techniques provide feedback about signal quality and characteristics to the operating mode selection logic. This feedback mechanism allows the system to adaptively adjust its processing complexity based on actual signal conditions, maintaining high reliability by selecting the most appropriate processing strategy for each scenario while minimizing unnecessary computational overhead.
3Measurement precision
If signal conditioning and filtering are applied, then measurement precision is improved, but loss of information increases
Solution Approach 1:
Different regions of the signal are treated with different processing intensities. The system applies signal conditioning and filtering selectively based on local signal characteristics identified through qualification techniques. This allows the system to improve measurement precision in problematic regions while preserving signal details in regions that are already of high quality, thereby minimizing overall information loss.
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 like low perfusion or dicrotic notches.
Implementation Method 1
A sensor, having a detector, generates an intensity 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 lag matrix, which includes multiple segments of the physiological signal each having the same number of values. The system may generate a correlation matrix, which includes multiple correlation values, based on the lag matrix. The system may identify a peak in the correlation lag matrix, or a processed matrix derived thereof, and the corresponding lag value. The correlation matrix, or processed matrix thereof, may be rotated, averaged, or otherwise transformed by the system to identify the lag value. The system may determine physiological rate information based on the identified lag value.