Physiological Rhythm Signal Processing for Unobtrusive Monitoring
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Solution Overview
Problem
Conventional algorithms for estimating instantaneous frequencies from physiological rhythms, such as heart rate or respiratory rate, are limited by requiring prior knowledge of signal morphology and are not suitable for unobtrusive sensors in uncontrolled environments, where signal quality can be unreliable due to varying orientations and positions of users.
Innovation Solution
A computer-implemented method for robust continuous local interval estimation that adapts to different physiological rhythms without prior knowledge of signal morphology, using multiple interval length estimation methods and weighting factors to improve accuracy and reliability, especially for unobtrusive sensors like bed-mounted BCG sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional algorithms based on detecting particular signal features (such as QRS complex in ECG) are used, then instantaneous frequency estimation can be achieved, but the method requires extensive prior knowledge about the expected morphology of the waveform and is limited to one type of signal
Solution Approach 1:
The patent applies universality by developing a method that can estimate instantaneous frequency across multiple signal types (ECG, BCG, pulse oximetry, respiratory inductance plethysmography) without requiring morphology-specific algorithms. The approach uses generic signal processing techniques that adapt to different waveform characteristics, making the system multi-functional rather than specialized for a single signal type.
Solution Approach 2:
The patent employs parameter changes by adjusting filtering parameters and analysis window characteristics based on the specific signal being analyzed, rather than requiring complete algorithm redesign. The method modifies existing parameters to accommodate different signal morphologies, enabling versatility while maintaining estimation accuracy.
2Ease of operation
If unobtrusive measurement systems (such as bed-mounted BCG sensors) are used to allow continuous long-term monitoring without user interaction, then user compliance requirements are eliminated, but signal quality becomes highly varying and unreliable due to uncontrolled environment and varying user orientation
Solution Approach 1:
The patent applies dynamics by implementing adaptive filtering and analysis parameters that automatically adjust to changing signal conditions caused by varying user orientations and positions. The system dynamically modifies its processing characteristics in response to real-time signal quality variations, maintaining reliability despite environmental uncontrolled conditions.
Solution Approach 2:
The patent employs feedback mechanisms where the estimated instantaneous frequency and signal quality metrics are continuously monitored and used to adjust processing parameters. This closed-loop approach allows the system to compensate for signal degradation caused by varying user positions, maintaining measurement reliability without requiring user compliance.
3Measurement precision
If algorithms are developed specifically for controlled clinical environments with stable signal morphology, then measurement precision can be maintained, but the algorithms cannot be applied outside clinical settings where signal morphology changes drastically
Solution Approach 1:
The patent applies preliminary action by implementing robust preprocessing steps including adaptive filtering and artifact removal that prepare the signal for analysis before instantaneous frequency estimation. These preliminary processing steps are designed to handle a wide range of signal conditions, ensuring that the core estimation algorithm receives pre-conditioned input regardless of environmental variations.
Solution Approach 2:
The patent uses dynamics by making the preprocessing parameters and analysis characteristics adaptive rather than fixed. The system automatically adjusts filtering bandwidth, window length, and other parameters based on the actual signal characteristics observed, allowing it to maintain precision across different environments without requiring environment-specific algorithm versions.
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AI summary
A method of processing a signal representing a physiological rhythm of a subject, the method comprising the steps of receiving the signal from the subject, filtering the signal with a band pass filter, extracting an analysis window from the filtered signal, performing a plurality of interval length estimation methods on the filtered signal in the analysis window, summing the outputs of the plurality of interval length estimation methods, and determining an interval length from the sum of the outputs of the plurality of interval length estimation methods.