Cardiac Signal Overdetection Correction via Morphology and Interval Analysis
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Solution Overview
Problem
Implantable cardiac devices face challenges in accurately analyzing cardiac signals, leading to overdetection of cardiac events, which results in incorrect heart rate calculations and inappropriate therapy decisions due to noise and variations in normal cardiac activity.
Innovation Solution
The implementation of methods and systems that identify and correct overdetection by categorizing detected events into suspect, overdetected, and certified events, using morphology, interval, and feature/proximity analysis to adjust detection profiles and thresholds, and applying rule sets to determine accurate cardiac rhythm classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If implantable cardiac devices use detection algorithms to identify cardiac events, then cardiac rhythm classification is enabled, but overdetection of cardiac events occurs due to noise and variations in normal cardiac activity
Solution Approach 1:
The patent segments the detection process into multiple independent analysis components: morphology analysis examines waveform characteristics, interval analysis measures time between events, and feature/proximity analysis evaluates specific signal features. Each segment operates with its own criteria and contributes independently to the overall detection decision, reducing the impact of noise on any single measurement.
Solution Approach 2:
The patent implements feedback mechanisms where detection results from one analysis method inform and adjust other analysis methods. The system continuously refines detection profiles based on accumulated data and identified patterns of overdetection, adjusting thresholds and parameters dynamically to improve accuracy while reducing false detections.
2Reliability
If detection thresholds are lowered to capture more cardiac events, then sensitivity increases, but overdetection increases due to noise
Solution Approach 1:
The patent applies different detection thresholds and analysis criteria to different characteristics of the cardiac signal. Morphology analysis uses shape-based criteria, interval analysis uses time-based criteria, and feature analysis uses amplitude-based criteria. Each analysis method has optimized local thresholds suited to its specific measurement type, allowing high sensitivity without proportionate increases in false detections.
3Reliability
If multiple detection methods are used to improve accuracy, then detection reliability increases, but device complexity increases
Solution Approach 1:
The patent divides the complex detection system into three separate, independently implementable analysis modules: morphology analysis, interval analysis, and feature/proximity analysis. Each module can be implemented as a distinct algorithmic component with its own data structures and processing logic, making the overall complex system manageable through modular design and independent optimization.
4Measurement precision
If detection profiles are adjusted to reduce overdetection, then false alarms decrease, but true cardiac events may be missed
Solution Approach 1:
The patent applies multiple independent detection methods with their own thresholds and criteria, requiring only a certain number of methods to agree on a detection (partial action). This approach is less stringent than requiring all methods to agree, thereby maintaining high sensitivity while reducing false detections through the redundancy of multiple analysis perspectives.
Data Source
AI summary
Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In illustrative examples, sensed data including detected events is analyzed to identify likely overdetection of cardiac events. In some illustrative examples, when overdetection is identified, data may be modified to correct for overdetection, to reduce the impact of overdetection, or to ignore overdetected data.