Dynamic Threshold Cerebral Blood Flow Monitoring for Consciousness Estimation
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Solution Overview
Problem
Current methods lack accuracy in estimating the likelihood of loss of consciousness, posing a danger to individuals and those around them, as they do not effectively predict when such events may occur.
Innovation Solution
A loss-of-consciousness estimation system that uses a biological sensor to measure cerebral blood flow rate, determining out-of-range data based on a threshold region calculated from a time series of cerebral blood flow correlation amounts, and estimates ventricular states to predict the likelihood of loss of consciousness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed threshold is used to determine out-of-range data in cerebral blood flow monitoring, then the device complexity is reduced, but the measurement precision and reliability of loss of consciousness estimation deteriorates
Solution Approach 1:
The patent implements dynamic threshold determination by calculating thresholds based on the distribution of cerebral blood flow data within a moving time window. Instead of using a fixed threshold, the system continuously adapts the threshold region according to the statistical distribution (mean and standard deviation) of recent data, allowing the threshold to change dynamically with the subject's physiological state while maintaining estimation accuracy
Solution Approach 2:
The system changes the threshold parameter from a fixed value to a dynamically calculated value based on data distribution statistics. By computing the threshold as mean ± k×standard deviation within a time window, the parameter adapts to varying physiological conditions, resolving the contradiction between simplicity and precision
2Measurement precision
If a dynamic threshold region based on data distribution is used, then the measurement precision improves, but the device complexity and computational load increase
Solution Approach 1:
The patent segments the cerebral blood flow data into discrete time windows and calculates threshold regions independently for each segment. This segmentation allows the complex calculation to be performed on manageable data portions rather than the entire dataset, reducing computational complexity while maintaining precision through localized statistical analysis
Solution Approach 2:
The system uses the data itself to determine the threshold region by calculating mean and standard deviation from the data distribution. The data serves its dual purpose of both being analyzed and providing the basis for threshold determination, eliminating the need for external reference standards or complex calibration procedures
3Reliability
If out-of-range data determination based on time-position-specific thresholds is implemented, then the reliability of ventricular state estimation improves, but the processing time and computational resources increase
Solution Approach 1:
The system implements periodic threshold calculation by dividing the data stream into fixed time windows and updating thresholds at regular intervals rather than continuously. This periodic approach maintains reliability by frequently updating thresholds while reducing computational burden compared to continuous real-time threshold adjustment
Solution Approach 2:
The threshold region is determined in advance for each time position based on the distribution of data within a preceding time window. By pre-calculating thresholds before actual loss of consciousness detection, the system prepares the evaluation criteria beforehand, reducing processing time during critical detection phases
Data Source
AI summary
An aspect of the present invention is a loss-of-consciousness estimation apparatus including: an out-of-range data determination unit configured to execute out-of-range data determination processing for, using an amount correlated with a cerebral blood flow rate of an estimation target as a cerebral blood flow correlation amount, a time series of the cerebral blood flow correlation amount as a cerebral blood flow correlation amount time series, and a position in a time axis direction of data of the cerebral blood flow correlation amount time series as a time position, determining whether or not the cerebral blood flow correlation amount indicated by each piece of the data is out of range of a threshold region, which is a range corresponding to the time position of each piece of the data, based on the cerebral blood flow correlation amount time series; and a ventricular state estimation unit configured to estimate a ventricular state of the estimation target based on the determination result of the out-of-range data determination unit, in which, before the execution of the out-of-range data determination processing, the out-of-range data determination unit executes processing for determining the threshold region of each time position, which is processing for determining the threshold region that is to be determined according to a distribution of the data in a first period, which is a period of a first length including the time position at which the threshold region is determined.