HRV and RRV Temporal Abstraction for Sepsis Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for early identification of sepsis in neonates, particularly in NICUs, face challenges due to complex data processing algorithms that are not transparent to end-users, lack flexibility, and are not suitable for real-time data analysis, especially in environments where high-frequency data acquisition is not feasible, leading to delayed diagnosis and potential over-reliance on software systems.
Innovation Solution
A system and method that utilize temporal abstraction analysis of Heart Rate Variability (HRV) and Respiratory Rate Variability (RRV) signals acquired in real-time, combined with clinical decision support, to identify potential condition onset, distinguishing between sepsis and confounding factors like surgery or narcotics, using lower granularity readings and a cloud-based platform for real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex data processing algorithms are used for early identification of sepsis, then identification accuracy is improved, but system complexity increases and transparency to end-users deteriorates
Solution Approach 1:
The patent extracts and emphasizes only the most critical HRV parameters (SDNN, RMSSD, pNN50) from the complex ECG data stream, presenting them as simplified metrics that maintain diagnostic accuracy while reducing computational complexity and improving interpretability for clinical users
Solution Approach 2:
The system segments the complex sepsis detection problem into distinct modules: HRV calculation module, parameter extraction module (SDNN, RMSSD, pNN50), and clinical decision support module, allowing each component to be optimized independently and improving overall system transparency
2Measurement precision
If high-frequency data acquisition is used for HRV analysis, then measurement precision is improved, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent changes the sampling frequency parameter from high-frequency continuous acquisition to lower-frequency interval-based sampling (e.g., every 30 seconds), maintaining adequate HRV measurement precision while significantly reducing data processing complexity and computational resource requirements
Solution Approach 2:
Instead of processing every individual heart beat signal in detail, the system applies partial action by focusing analysis on aggregated HRV parameters over time intervals, achieving sufficient measurement precision without the excessive computational burden of full high-frequency processing
3Loss of time
If real-time data analysis is implemented, then response time is improved, but computational resources and system complexity increase
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing HRV parameters (SDNN, RMSSD, pNN50) and their reference ranges before clinical decisions are needed, enabling rapid real-time assessment without complex computational burden during critical moments
Solution Approach 2:
The system implements self-service through automated HRV calculation and clinical decision support that operates autonomously without requiring complex real-time computational resources or human intervention, reducing both response time and system complexity
4Measurement precision
If multiple physiological parameters are monitored simultaneously, then diagnostic accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple physiological monitoring functions into a unified HRV-based diagnostic approach, where heart rate variability analysis integrates information from ECG signals to simultaneously assess cardiac function, autonomic nervous system status, and sepsis risk, reducing overall data processing complexity while maintaining high diagnostic accuracy
Data Source
AI summary
The present disclosure relates to a method and system for identifying potential condition onset based upon a combination of Heart Rate Variability (HRV), Respiratory Rate Variability (RRV) and/or confounding factors. The present method and system may involve data collection, a temporal abstraction (TA)-based approach, and data analysis to identify potential condition onset in patients. The present method and system may generate and amend a classification scheme to be operable to determine that a patient is facing potential condition onset. The present method and system may further be operable to provide clinical decision making support. Embodiments of the present method and system may be operable to identify trends, such as temporal patterns, and to undertake a variety of analyses of collected and/or TA data to provide indicators, and determinations of potential condition onset in patients. As an example, the present method and system may be applied to identify potential condition onset of sepsis in infant patients.