Autoregulation Zone Identification via Cerebral Oximetry Index
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Solution Overview
Problem
Existing systems for monitoring autoregulation in patients are inefficient and unreliable, making it difficult to identify autoregulation zones and determine target blood pressures, which are crucial for maintaining optimal cerebral blood flow and preventing conditions like ischemia or hyperemia.
Innovation Solution
A system that uses blood pressure and oxygen saturation sensors, along with data clustering algorithms like k-means and Gaussian mixture models, to identify autoregulation zones and determine target blood pressures by processing signals from sensors to derive a cerebral oximetry index, facilitating efficient and reliable monitoring of autoregulation status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems and methods are used to determine autoregulation status, then monitoring can be performed, but the determination is inefficient and unreliable
Solution Approach 1:
The patent segments the continuous physiological data into discrete autoregulation zones (lower impaired, intact, upper impaired) based on blood pressure thresholds. This segmentation allows for more reliable and efficient determination of autoregulation status by categorizing complex physiological states into distinct, manageable zones that can be systematically monitored and evaluated.
2Measurement precision
If complex data processing algorithms are implemented to improve monitoring accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer that receives raw physiological data from sensors and transforms it into clinically meaningful autoregulation zone classifications. This intermediary system uses established blood pressure threshold criteria to bridge the gap between raw data and clinical interpretation, improving measurement precision while managing device complexity through standardized processing protocols.
Data Source
AI summary
A system configured to monitor autoregulation includes a medical sensor configured to be applied to a patient and to generate a regional oxygen saturation signal. The system includes a controller having a processor configured to receive the regional oxygen saturation signal and a blood pressure signal and to determine a cerebral oximetry index (COx) based on the blood pressure signal and the regional oxygen saturation signal. The processor is also configured to apply a data clustering algorithm to cluster COx data points over a range of blood pressures, identify a first cluster of COx data points that corresponds to an intact autoregulation zone for the patient, and provide a first output indicative of the intact autoregulation zone for the patient.