Composite Indicator for Worsening Heart Failure Detection
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Solution Overview
Problem
Current ambulatory medical devices face challenges in detecting worsening congestive heart failure (CHF) due to the complexity of combining signals from multiple physiological sensors, which leads to high false positive rates and increased healthcare costs.
Innovation Solution
A system that generates a predictor trend from physiological signals and transforms it into transformed indices using a codebook with threshold pairs, allowing for the detection of worsening CHF events with improved sensitivity and specificity, reducing false positives and operational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple physiological sensors are used to detect worsening heart failure, then detection sensitivity is improved, but false positive rate increases
Solution Approach 1:
The patent combines multiple physiological sensor signals (impedance, temperature, motion, etc.) into a single composite indicator through signal processing algorithms. This merging approach allows the system to leverage the complementary information from different sensors to improve detection sensitivity while reducing false positives through cross-validation of multiple signal sources.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw sensor signals into a composite indicator through algorithms including signal filtering, feature extraction, and pattern recognition. This intermediary layer reconciles the conflicting requirements by synthesizing multiple signals in a way that enhances true event detection while filtering out false alarms.
2Measurement precision
If multiple physiological sensors are used to detect worsening heart failure, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple complex sensor signals into a single composite indicator, effectively reducing the complexity of signal processing and interpretation while maintaining high detection accuracy. This consolidation simplifies the device architecture and data management requirements.
Solution Approach 2:
The patent creates a universal composite indicator that can be derived from various combinations of physiological sensors, making the detection system adaptable to different sensor configurations without requiring separate processing algorithms for each sensor type. This multi-functional approach reduces overall system complexity.
3Measurement precision
If individually adjusted detection thresholds are used for each sensor signal, then detection precision is improved, but data interpretability decreases
Solution Approach 1:
The patent combines multiple individually thresholded sensor signals into a single composite indicator with unified interpretation criteria. This approach preserves the precision benefits of individual threshold adjustments while restoring data interpretability by providing a single, clear output metric that clinicians can easily understand and act upon.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances the accuracy and efficiency of CHF detection, reducing unnecessary interventions and healthcare costs by improving data interpretability and inter-sensor comparability, while also extending device battery life.
Implementation Method 1
The ambulatory medical devices may include physiological sensors which may be configured to sense electrical activity and mechanical function of the heart
Data Source
AI summary
Systems and methods for detecting a target cardiac condition such as events indicative of worsening heart failure are described. A system may include sensor circuits for sensing physiological signals and a signal processor for generating a predictor trend indicative of temporal change of the physiological signal. The predictor trend may be transformed into a sequence of transformed indices using a codebook that includes a plurality of threshold pairs each including onset and reset thresholds. The codebook may be constructed and updated using physiological data. The system may detect target cardiac condition using the transformed indices.