Cerebral Autoregulation Monitoring via Filtered BP-SpO2 Correlation
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Solution Overview
Problem
Existing techniques fail to provide real-time evaluation of cerebral autoregulation during surgery, which is crucial for predicting and preventing neurological complications in patients with impaired cerebral autoregulation, such as moyamoya disease.
Innovation Solution
An apparatus and method utilizing a data acquisition unit for blood pressure and oxygen saturation data, correlation coefficient calculation, filtering with a moving average filter, and a cerebral autoregulation evaluation unit to assess cerebral autoregulation in real time, with a time window of 25-30 minutes, and an optional alarm for abnormal states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time evaluation of cerebral autoregulation is implemented during surgery, then postoperative complications can be predicted and prevented, but existing techniques fail to provide this capability
Solution Approach 1:
The evaluation system is segmented into distinct functional modules: a data acquisition unit for collecting blood pressure and oxygen saturation data, a correlation coefficient calculation unit for processing the data, a filtering unit for noise reduction, and an evaluation unit for assessing cerebral autoregulation. This modular segmentation enables real-time monitoring while managing system complexity through organized functional divisions.
Solution Approach 2:
The system implements continuous feedback by calculating correlation coefficients between blood pressure and oxygen saturation data in real-time, filtering these coefficients, and using them to evaluate cerebral autoregulation status. This closed-loop feedback mechanism allows dynamic monitoring and prediction of postoperative complications, improving reliability through ongoing assessment rather than static preoperative evaluation.
2Measurement precision
If correlation coefficients are calculated continuously for real-time evaluation, then evaluation accuracy improves, but calculation time and processing load increase
Solution Approach 1:
The system performs correlation coefficient calculations at periodic intervals rather than continuously, evaluating data over specific time periods (e.g., 25-30 minute windows). This periodic approach maintains measurement precision by using sufficient data samples while reducing processing time and computational load compared to truly continuous calculation.
Solution Approach 2:
The system pre-calculates correlation coefficients over defined time periods before final evaluation, preparing processed data in advance. This preliminary action allows the evaluation unit to work with pre-processed correlation coefficients rather than raw data, improving evaluation accuracy while reducing real-time processing time.
3Stability of the object's composition
If a moving average filter with a 25-30 minute time window is used, then evaluation stability improves, but real-time responsiveness decreases
Solution Approach 1:
The system optimizes the filter time window parameter to 25-30 minutes, balancing stability and responsiveness. This specific parameter range is sufficient to capture meaningful physiological variations in cerebral autoregulation while limiting the window size to maintain reasonable response speed. The parameter is tuned based on the physiological characteristics of cerebral blood flow regulation.
Data Source
AI summary
An apparatus for evaluating cerebral autoregulation includes a data acquisition unit configured to acquire blood pressure data and oxygen saturation data of a patient undergoing surgery, a correlation coefficient calculation unit configured to calculate correlation coefficients between the acquired blood pressure data and the acquired oxygen saturation data, a filtering unit configured to filter the calculated correlation coefficients using a moving average filter having a predetermined time window, and a cerebral autoregulation evaluation unit configured to evaluate the cerebral autoregulation of the patient undergoing surgery based on the filtered correlation coefficient.