Multivariate Epilepsy Risk Detection System
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Solution Overview
Problem
Current medical device systems for epilepsy patients are inadequate in accurately estimating the risk of sudden unexpected death in epilepsy (SUDEP) due to limitations in monitoring multiple biologic signals over varying time scales and failing to distinguish between SUDEP and sleep apnea syndromes, leading to high false positive rates and potential increased risk of death.
Innovation Solution
A medical device system that receives and analyzes cardiac, arousal, and responsiveness data to determine extreme values indicative of an increased risk of death, issuing warnings and logging information in real-time, using multivariate adaptive methods to incorporate various autonomic, neurologic, and metabolic signals over multiple time scales, including seizure severity, inter-seizure intervals, and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If univariate monitoring of respiratory signals is used to detect SUDEP risk, then the system is simple to operate, but it produces high false positive rates and cannot distinguish SUDEP from sleep apnea syndromes
Solution Approach 1:
The patent combines multiple biologic signals (respiratory, cardiac, arousal, responsiveness) into a multivariate monitoring system. This merging of signals allows the system to distinguish between SUDEP and sleep apnea syndromes by analyzing patterns across multiple physiological domains simultaneously, thereby reducing false positive rates while maintaining ease of operation through automated integrated analysis.
Solution Approach 2:
The monitoring system is designed to perform multiple functions: detecting SUDEP risk, distinguishing it from sleep apnea, monitoring seizure activity, and tracking various physiological parameters. This multi-functional approach enables a single system to provide comprehensive epilepsy management while maintaining operational simplicity through centralized control.
2Measurement precision
If multivariate adaptive methods incorporating multiple biologic signals over multiple time scales are used, then measurement precision of risk estimation is improved, but device complexity increases
Solution Approach 1:
The patent segments the monitoring system into distinct functional modules: signal acquisition units for different biologic signals, processing units for analyzing specific signal types, and integration units for combining results. This segmentation allows the complex multivariate analysis to be performed through coordinated simpler subsystems, improving measurement precision while managing device complexity through modular architecture.
Solution Approach 2:
The system adds temporal dimensionality by analyzing signals across multiple time scales (short-term seizure detection, medium-term pattern recognition, long-term risk assessment). This multi-temporal approach enhances measurement precision by capturing dynamics at different levels without requiring proportional increases in device complexity, as the same hardware processes signals at multiple temporal resolutions.
3Measurement precision
If the system monitors multiple physiological parameters and environmental factors, then the accuracy of risk assessment is improved, but the quantity of data to be processed increases
Solution Approach 1:
The system extracts only the most clinically relevant features from the multitude of collected signals and environmental data. By identifying and extracting key discriminative features (such as specific respiratory patterns, cardiac anomalies, arousal responses) while discarding redundant information, the system maintains high risk assessment accuracy while reducing the quantity of data requiring detailed processing and storage.
4Speed
If real-time warning and logging functions are implemented, then responsiveness to increased risk is improved, but use of energy increases
Solution Approach 1:
The system implements periodic analysis of biologic signals at strategically determined intervals rather than continuous processing. Real-time warning functions are triggered by event detection (seizures, abnormal patterns) rather than constant monitoring, and logging occurs at clinically relevant time points. This periodic operation maintains responsiveness to critical events while significantly reducing energy consumption compared to continuous real-time processing.
Data Source
AI summary
A method for determining and responding in real-time to an increased risk of death relating to a patient with epilepsy is provided. The method includes receiving cardiac data and determining a cardiac index based upon the cardiac data. The method includes determining an increased risk of death associated with epilepsy if the indices are extreme, issuing a warning of the increased risk of death and logging information related to the increased risk of death. Also presented is a second method for determining and responding in real-time to an increased risk of death relating to a patient with epilepsy comprising receiving at least one of arousal data, responsiveness data or awareness data and determining an arousal index, a responsiveness index or an awareness index, where the indices are based on arousal data, responsiveness data or awareness data respectively. The second method includes determining an increased risk of death related to epilepsy if indices are extreme values, issuing a warning of the increased risk of death and logging information related to the increased risk of death. A computer readable program storage device is also provided. Also provided is a method for receiving body data, determining a cardiac, an arousal, a responsiveness, or a kinetic index, determining an increased or increasing risk of death over a first time window relating to a patient with epilepsy and issuing a warning and logging relevant information.