Respiration Rate Distribution Trend Parameter for Heart Failure Monitoring
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Solution Overview
Problem
Current methods fail to effectively monitor and manage heart failure status, particularly in distinguishing between stable and decompensated heart failure, due to the lack of reliable and quantifiable parameters for respiratory rate analysis.
Innovation Solution
A system and method for generating a trend parameter based on the distribution of a patient's respiration rate, using sensors and processors to detect respiratory parameters, calculate respiration rate distributions, and produce a trend parameter that can differentiate between stable and decompensated heart failure states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional respiration monitoring methods are used, then the monitoring system is simple, but the ability to distinguish between stable and decompensated heart failure is insufficient
Solution Approach 1:
The patent transforms the monitoring approach by changing from simple respiration rate measurement to analyzing the distribution characteristics of respiration rates. This involves calculating statistical parameters (mean, standard deviation, skewness, kurtosis) from the respiration rate distribution, which provides more reliable differentiation between stable and decompensated heart failure states while maintaining computational simplicity
Solution Approach 2:
The invention adds a new dimension to respiration monitoring by introducing the concept of respiration rate distribution analysis. Instead of monitoring only the mean respiration rate, the system analyzes the entire distribution profile across multiple bins, creating a more comprehensive assessment dimension that improves diagnostic reliability without requiring complex hardware
2Measurement precision
If respiration rate distribution analysis is implemented, then the accuracy of heart failure status monitoring is improved, but the computational complexity increases
Solution Approach 1:
The patent resolves the computational complexity issue by transforming the distribution analysis into calculations of standard statistical parameters (mean, standard deviation, skewness, kurtosis). These parameters can be computed efficiently from binned respiration rate data and provide sufficient discrimination power for clinical decision-making, balancing precision with computational feasibility
Solution Approach 2:
The system segments the respiration rate range into multiple bins and analyzes the distribution across these segments. This segmentation approach simplifies the computational burden by discretizing the continuous respiration rate data, making it easier to process while still capturing the essential distribution characteristics needed for accurate heart failure status detection
3Reliability
If only mean respiration rate is monitored, then the monitoring system is simple to operate, but it cannot effectively detect rapid shallow breathing patterns
Solution Approach 1:
The patent addresses the limitation of mean respiration rate monitoring by introducing distribution-based parameters that specifically capture rapid shallow breathing patterns. The skewness and kurtosis parameters, in particular, are sensitive to the presence of frequent low-rate breaths, providing reliable detection of RSB while maintaining straightforward implementation through automated calculation
Data Source
AI summary
Systems and methods provide for assessing the heart failure status of a patient and, more particularly, to generating a trend parameter based on a distribution of the patient's respiration rate. Systems and methods provide for detecting, using an implantable device or a patient-external device, patient respiration and computing a respiration rate based on the detected patient respiration. A distribution of the respiration rate is calculated, and a trend parameter based on the respiration rate distribution is generated. The trend parameter is indicative of a patient's heart failure status. An output signal indicative of the patient's heart failure status may be generated based on the trend parameter.