Physiological Rhythm Signal Estimation via Prior Probability Refinement
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Solution Overview
Problem
Unobtrusive and unconstrained measurement systems for physiological rhythms face challenges in providing reliable and accurate estimations of instantaneous frequencies due to varying signal quality and changes in posture or movement.
Innovation Solution
A computer-implemented method that processes a signal representing a physiological rhythm by estimating a first group of period characteristic estimations, generating a prior probability based on a subset of these estimations, and then estimating a second group of period characteristic estimations using the prior probability, thereby improving accuracy especially in cases of movement or posture change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If unobtrusive measurement systems are used for continuous monitoring, then user compliance and comfort are improved, but signal quality and measurement reliability deteriorate due to uncontrolled environments and posture changes
Solution Approach 1:
The system performs preliminary actions by continuously estimating instantaneous frequencies and building a probability distribution model before actual measurement needs arise. This allows the system to pre-characterize the relationship between sensor readings and physiological parameters under various conditions, enabling more reliable measurements when actually needed without requiring controlled environments or user compliance during the measurement process itself
Solution Approach 2:
The system implements feedback by using estimated instantaneous frequencies to update and refine the probability distribution model over time. This continuous feedback loop allows the system to adapt to individual user characteristics and environmental variations, improving measurement reliability while maintaining the unobtrusive nature of the monitoring system
2Measurement precision
If conventional algorithms based on feature detection are used, then measurement precision is improved for controlled signals, but adaptability deteriorates when signal morphology changes due to movement or posture changes
Solution Approach 1:
The system applies parameter changes by transforming the measurement approach from direct feature detection to probabilistic parameter estimation. Instead of relying on fixed morphological features that change with posture, the system estimates instantaneous frequencies and uses these to build a probability distribution that adapts to different signal conditions, maintaining measurement precision across varying morphologies
Solution Approach 2:
The system achieves universality by creating a measurement approach that works across multiple signal types and conditions. The probability distribution model can handle different physiological rhythms and signal morphologies uniformly, making the system adaptable to various postures and movement states while maintaining consistent measurement precision through the unified probabilistic framework
Data Source
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AI summary
There is provided a method to estimate a period characteristic, such as period length or a period location, of a physiological rhythm, such as a cardiac rhythm or a spontaneous breathing rhythm. The method comprises receiving the signal representing the physiological rhythm, estimating a first group of period characteristic estimations based on the signal, generating a prior probability for the period characteristic based on at least a subset of the first group of period characteristic estimations, and estimating a second group of period characteristic estimations based on the first group and the prior probability. By using this method, the accuracy of the signal is improved, especially when the signal is obtained from an accelerometer arranged on the chest of the subject.