Non-linear physiological signal analysis for early sepsis detection
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Solution Overview
Problem
Current medical technologies lack effective methods for early and individualized risk stratification of patients with rapidly evolving conditions like sepsis, cardiac arrhythmias, and heart failure, leading to delayed interventions and high mortality rates, particularly in ICU settings, due to the complexity and volume of physiological data exceeding cognitive assimilation and analytical capabilities.
Innovation Solution
A method and system for continuous monitoring and analysis of time-varying physiological signals using linear and non-linear analyses to identify unique prognostic signatures, integrating data from various sources, including wearable devices and ICU monitors, to provide early prediction of impending adverse events and guide personalized medical care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional clinical measures and aggregate-based risk scores are used, then the system is simple and easy to implement, but it cannot detect subclinical deterioration signatures and provides insufficient early warning capability
Solution Approach 1:
The patent segments physiological monitoring into multiple layers: conventional clinical measures for basic assessment, and advanced non-linear analyses (entropy calculations, fractal dimensions, spectral analysis) for detecting subclinical patterns. This segmentation allows the system to maintain simplicity for routine monitoring while providing sophisticated early detection when needed.
Solution Approach 2:
The patent introduces new dimensions of analysis by applying non-linear mathematical transformations to physiological signals. Instead of only analyzing amplitude and frequency, the system calculates entropy, fractal dimensions, and spectral characteristics, adding informational dimensions that reveal hidden patterns of subclinical deterioration.
2Loss of information
If massive physiological data from multiple sources are collected, then the data volume increases providing more information, but it exceeds cognitive assimilation and analytical capabilities
Solution Approach 1:
The patent extracts specific informative features from massive physiological datasets using non-linear analysis methods. Instead of attempting to process all raw data, the system extracts key characteristics such as entropy values, fractal dimensions, and spectral parameters that capture essential information about system stability and predictability.
Solution Approach 2:
The patent transforms physiological parameters through non-linear mathematical operations. By calculating entropy, fractal dimensions, and spectral characteristics, the system changes the parameter representation from raw physiological values to information-rich derived parameters that reveal hidden patterns while reducing data dimensionality.
3Reliability
If early diagnosis and therapy are implemented, then clinical outcomes improve and mortality decreases, but the ability to predict individual mortality risk and identify high-risk patients is limited
Solution Approach 1:
The patent implements feedback mechanisms where non-linear analysis of physiological signals provides continuous information about system stability and predictability. This feedback loop allows dynamic adjustment of risk assessment, enabling the system to identify deteriorating patterns before they manifest as clinical events and to stratify individual patient risk more accurately.
Solution Approach 2:
The patent performs preliminary analysis of physiological signals using non-linear methods to detect early signs of instability before clinical deterioration occurs. By calculating entropy and fractal dimensions in advance, the system identifies high-risk patients who would benefit from early intervention, enabling preliminary risk stratification and preventive action.
Data Source
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AI summary
One or more time-varying signals (502, 504, 506, 510) from continuous monitoring of an individual or the individual's environment are processed in a non-linear fashion to develop signatures (552) for those signals to assess the individual's health. Qualitative data (508) such as individual, family, and/or health care provider reporting of activity or status, and lab data (512), may also be used. The system may compute an integrated likelihood of the individual experiencing an illness or condition, which is provided (560, 562) to the individual and/or a training or health-care provider for the individual, and updated on a time schedule, giving pre- symptomatic notice of illnesses and early identification of conditions. The system may also optimize a course of performance training and diet. Further, by incorporating treatment data, the invention may be used in forming a quality measure of the individual's care, health, function, risk of adverse or undesired event, and efficacy or lack thereof of medical treatments or other necessary interventions.