Cardiac Rhythm Classification via Correlation Analysis
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Solution Overview
Problem
Implantable cardiac devices face challenges in accurately classifying cardiac rhythms due to overdetection of cardiac events, leading to incorrect heart rate calculation and therapy decisions.
Innovation Solution
The use of correlation analysis to identify overdetection by comparing cardiac signals to templates, adjusting alignment, and modifying stored data to correct rate analysis, with multiple boundaries and features for correlation scoring, and inhibiting data correction based on predetermined thresholds and interval analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correlation analysis with multiple boundaries is used to identify overdetection, then measurement precision of cardiac events is improved, but device complexity increases
Solution Approach 1:
The patent divides the detection problem into segments by using multiple correlation boundaries (first boundary and second boundary) to classify detected events. Events are segmented into different categories based on which boundary they exceed, allowing systematic identification of overdetections without requiring a single complex thresholding mechanism.
Solution Approach 2:
The patent applies partial action by using a tiered approach where only events exceeding the first (higher) boundary trigger detailed analysis. The second (lower) boundary provides an additional layer of filtering, but the system doesn't require all possible analysis methods to be applied to every event, only to those that meet specific criteria.
2Measurement precision
If template alignment is adjusted by shifting samples to maximize correlation score, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary alignment by identifying fiducial points in the template and detected events before correlation analysis. This preliminary positioning allows the system to start the correlation calculation from an already-optimized alignment point, reducing the number of shifts needed to find the maximum correlation score.
Solution Approach 2:
The system skips unnecessary correlation calculations by using the fiducial point alignment to jump directly to the most likely optimal alignment. Instead of checking every possible sample shift, the method rushes through the process by calculating correlation scores only for shifts near the fiducial point alignment, significantly reducing processing time.
3Measurement precision
If multiple features are used for correlation analysis with several alignment points, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the correlation analysis into multiple independent features, each with its own alignment point. Instead of using a single complex alignment mechanism, the system divides the analysis into separate feature comparisons (e.g., R-wave alignment, T-wave alignment), where each feature can be processed independently and combined for final classification.
Solution Approach 2:
The fiducial point identification mechanism serves multiple functions: it provides alignment for correlation analysis, serves as an anchor for template matching, and enables the identification of key morphological features. This multi-functional approach reduces the need for separate mechanisms for each task, managing complexity while maintaining precision.
4Reliability
If data correction is inhibited based on interval analysis and predetermined thresholds, then reliability of therapy decisions is improved, but productivity decreases
Solution Approach 1:
The patent applies preliminary anti-action by pre-calculating expected QT intervals using a formula and comparing detected intervals against these expectations before finalizing heart rate calculations. This preliminary check prevents incorrect data correction from propagating through the system, ensuring that only intervals meeting physiological expectations are used in therapy decisions.
Solution Approach 2:
The system uses feedback by continuously monitoring whether corrected intervals meet predetermined thresholds and expected physiological ranges. If corrected intervals fall outside expected ranges, the system feedbacks to inhibit further correction and flags the event for manual review, ensuring reliability while maintaining automated processing for normal cases.
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 some examples, captured 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. Several examples emphasize the use of morphology analysis using correlation to static templates and/or inter-event correlation analysis.