Pulsating Signal Smoothing for Reduced Physiological Error
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Solution Overview
Problem
Conventional systems for deriving physiological parameters from pulsating signals, such as photoplethysmogram (PPG) signals, face challenges due to noise and high resource usage, particularly in low-power devices like mobile phones, leading to increased errors and computational resource usage, which hinders noninvasive and affordable healthcare monitoring.
Innovation Solution
A method and system that extract pulsating signals, smooth them using different time window lengths, derive local minima and maxima points, and calculate physiological parameters like pulse duration and peak-to-peak distance, reducing errors and optimizing resource usage by employing a signal extraction module, smoothening module, maxima derivation module, and statistical learning module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signal processing methods are used to extract physiological parameters from pulsating signals, then the derivation can be performed, but the error rate increases and computational resource usage increases
Solution Approach 1:
The patent segments the noisy pulsating signal into multiple time windows and processes each segment separately using different window lengths. This segmentation allows for localized noise filtering while maintaining computational efficiency, as each segment is processed independently rather than requiring heavy global processing of the entire signal.
Solution Approach 2:
The patent dynamically adjusts the time window length based on the characteristics of the signal being processed. By using variable window lengths adapted to local signal properties, the system optimizes noise filtering performance while minimizing computational resources required, avoiding the need for fixed heavy-processing approaches.
2Measurement precision
If noise filtering is applied to pulsating signals, then measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides the signal processing task into multiple segments using different time window lengths. This segmentation transforms a single complex filtering operation into multiple simpler localized operations, reducing overall computational complexity while maintaining or improving measurement accuracy through adaptive local processing.
Solution Approach 2:
The patent changes the parameter of time window length to optimize the balance between noise filtering effectiveness and computational complexity. By adjusting this parameter dynamically based on signal characteristics, the system achieves accurate physiological parameter derivation without requiring excessively complex processing algorithms.
3Measurement precision
If multiple processing windows are used for smoothening, then error reduction is achieved, but processing time increases
Solution Approach 1:
The patent segments the processing into parallel operations on different time windows, which can be executed simultaneously or in an optimized sequence. This segmentation approach reduces total processing time compared to sequential single-window processing, while still achieving error reduction through the combined results of multiple window analyses.
Solution Approach 2:
The patent applies partial processing by using different window lengths selectively based on local signal characteristics rather than uniformly applying heavy processing to the entire signal. This partial action approach achieves sufficient error reduction without the time cost of exhaustive processing of all signal portions with maximum complexity.
Data Source
AI summary
This disclosure relates generally to biomedical signal processing, and more particularly to method and system for physiological parameter derivation from pulsating signals with reduced error. In this method, pulsating signals are extracted, spurious perturbations in the extracted pulsating signals are removed for smoothening, local minima points in the smoothened pulsating signal are derived, systolic maxima point between two derived local minima are derived, most probable pulse duration and most probable peak-to-peak distance are derived, dicrotic minima is removed while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, diastolic peak is derived while ensuring that every dicrotic maxima is preceded by a diastolic notch followed by next beat start point of that maxima, and physiological parameters are derived from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.