Autoregulation Monitoring via Wavelet Phase Coherence
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Solution Overview
Problem
Existing systems for monitoring autoregulation in patients are prone to inaccuracies due to noise in physiological signals and other sources of error, leading to unreliable cerebral blood flow assessments.
Innovation Solution
A system that determines a confidence level for the cerebral oximetry index by analyzing the phase difference between blood pressure and oxygen saturation signals, allowing for the identification and correction of unreliable data points to provide more accurate autoregulation monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems determine autoregulation status based on correlation between blood pressure and oxygen saturation changes, then autoregulation monitoring is provided, but measurement accuracy deteriorates due to noise in physiological signals and other sources of error
Solution Approach 1:
The patent introduces wavelet phase coherence analysis as an intermediary method to process the relationship between arterial blood pressure and cerebral oxygen hemoglobin signals. This intermediary approach transforms the raw physiological signals into a more reliable metric that is less susceptible to noise and artifacts, thereby improving measurement precision while maintaining monitoring reliability.
Solution Approach 2:
The patent changes the parameter used for autoregulation assessment from simple correlation coefficients to wavelet phase coherence metrics. This parameter transformation allows the system to capture the dynamic relationship between blood pressure and oxygen saturation while filtering out noise, thus improving both measurement precision and reliability simultaneously.
2Duration of action of moving object
If physiological signals are used to monitor autoregulation, then continuous monitoring is achieved, but signal noise increases leading to inaccurate readings
Solution Approach 1:
Wavelet phase coherence analysis serves as an intermediary processing layer that continuously processes physiological signals over extended periods. This intermediary approach maintains the continuous monitoring capability while systematically filtering out noise and artifacts that accumulate over time, thereby preserving measurement precision throughout the monitoring duration.
Solution Approach 2:
The patent applies preliminary signal processing through wavelet transformation and phase coherence analysis before final autoregulation assessment. This preliminary action prepares the signals by removing noise and artifacts in advance, ensuring that subsequent measurements remain precise even during continuous long-term monitoring.
Data Source
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AI summary
A method 100 for monitoring autoregulation includes receiving a blood pressure signal and an oxygen saturation signal 102, 104, determining a phase difference between the blood pressure signal and the oxygen saturation signal 106, and determining a patient's autoregulation status based at least in part on a phase difference between the blood pressure signal and the oxygen saturation signal 108.