Adaptive Waveform Appraisal in Implantable Cardiac Systems
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Solution Overview
Problem
Implantable cardiac devices face challenges in accurately differentiating noise from cardiac events due to various sources of noise in sensed signals, making it difficult to classify cardiac activity as benign or malignant effectively.
Innovation Solution
An implantable cardiac system employs a set of noise analysis rules that dynamically adapt based on sensing and detection parameters, modifying the analysis window and noise analysis rules in response to changing conditions, allowing for more accurate differentiation between noise and cardiac events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed noise analysis rules are used, then device complexity is reduced, but measurement precision deteriorates due to inability to differentiate noise from cardiac events accurately
Solution Approach 1:
The patent implements dynamic noise analysis rules that automatically adapt to changing signal conditions. The system modifies analysis parameters such as window length and threshold values based on detected event rates and signal characteristics, transforming static analysis into a dynamic response system that maintains high precision without requiring complex manual configuration
Solution Approach 2:
The system changes key parameters including analysis window length and detection thresholds based on detected conditions. When event rates increase or signal characteristics change, the system automatically adjusts these parameters to maintain optimal detection accuracy, resolving the contradiction between fixed simplicity and adaptive precision
2Measurement precision
If dynamic adaptation of noise analysis rules is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system performs self-adjustment by automatically monitoring its own detection performance and modifying noise analysis rules based on detected patterns. The device uses its own operational data to tune parameters, eliminating the need for external programming or complex user intervention while maintaining high detection accuracy
Solution Approach 2:
The system incorporates feedback loops where detection results inform subsequent analysis rule adjustments. By continuously monitoring detected event rates and signal characteristics, the system feeds this information back into the noise analysis rule modification process, creating a self-optimizing system that improves precision without proportionally increasing complexity
3Adaptability or versatility
If analysis window length is modified in response to event rate, then adaptability improves, but loss of time increases due to dynamic parameter changes
Solution Approach 1:
The system pre-establishes multiple noise analysis rule sets corresponding to different event rate conditions. Rather than calculating optimal parameters in real-time, the system has pre-computed rule sets ready for immediate selection based on current event rates, eliminating computation time while maintaining adaptability
Solution Approach 2:
The system periodically evaluates whether parameter changes are needed based on sustained changes in event rates rather than responding to every fluctuation. This periodic assessment approach reduces unnecessary parameter modifications while maintaining responsiveness to genuine condition changes, balancing adaptability with time efficiency
Data Source
AI summary
Methods and implantable devices for cardiac signal analysis. The methods and devices make use of waveform appraisal techniques to distinguish event detections into categories for suspect events and waveform appraisal passing events. When adjustments are made to the data entering analysis for waveform appraisal, the waveform appraisal thresholds applied are modified as well. For example, when the data analysis window for waveform appraisal changes in length, a waveform appraisal threshold is modified. Other changes, including changes in sensing characteristics with which waveform appraisal operates may also result in changes to the waveform appraisal threshold including changes in gain, sensing vector, activation of other devices, implantee posture and other examples which are explained.