SUDEP Risk Assessment via ECG-EEG Statistical Association
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Solution Overview
Problem
Current methods for predicting sudden unexpected death in epilepsy (SUDEP) are unreliable due to limited predictive value of existing risk factors, failing to accurately identify patients at risk for this life-threatening condition.
Innovation Solution
A method and system that combine electrocardiographic (ECG) and electroencephalographic (EEG) data analysis to determine a statistical measure of association, using features like coherence and entropy, to identify an increased risk of SUDEP by exceeding a predetermined threshold, potentially indicating a higher risk through a processor-based system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If demographic and physiological risk factors are used to predict SUDEP, then some predictive information is obtained, but the predictive value remains limited and unreliable
Solution Approach 1:
The patent combines multiple data sources including demographic parameters, physiological parameters, ECG data, and EEG data into a unified predictive model. This integration allows the system to leverage the strengths of each data type while compensating for their individual limitations, thereby improving both predictive accuracy and reliability of SUDEP risk assessment
Solution Approach 2:
The patent transforms raw medical data into standardized risk scores through mathematical processing and statistical analysis. By converting diverse parameters (ECG intervals, EEG patterns, demographic factors) into a unified risk metric, the system enables more precise and reliable prediction of SUDEP susceptibility
2Loss of information
If multiple risk factors are analyzed, then more information is available, but the complexity of the assessment increases
Solution Approach 1:
The patent divides the comprehensive risk assessment into distinct modular components: demographic assessment, physiological assessment, ECG analysis, and EEG analysis. Each module processes specific data types independently and contributes to the overall risk score, making the complex assessment more manageable and interpretable while retaining information completeness
Solution Approach 2:
The patent creates a multi-functional predictive system that can process various types of medical data (demographic, physiological, ECG, EEG) through a unified analytical framework. This universal approach allows the system to handle diverse information sources efficiently without proportionally increasing operational complexity
Data Source
AI summary
A method for determining an increased risk of death of a patient includes receiving ECG data of the patient generated during a first time period; receiving EEG data of the patient generated during the first time period; composing a feature of the ECG data and a feature of the EEG data over a common time frame and determining a statistical measure of association between the ECG data and the EEG data; and determining whether the degree of association exceeds a predetermined threshold, thereby indicating whether an increased risk is present.