Cardiac Rate Calculation Using Interval Filtering
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Solution Overview
Problem
Implantable cardiac devices face challenges in accurately calculating cardiac rates due to overdetection, leading to inappropriate classification of cardiac activity and potential incorrect therapy decisions.
Innovation Solution
The method involves identifying and excluding selected intervals from the set of detected intervals to estimate cardiac rate, using techniques such as the 5/8 Interval Method, which discards the longest and shortest intervals to reduce the impact of overdetection and improve rate calculation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all detected intervals are used to calculate cardiac rate, then the calculation is simple and fast, but the accuracy deteriorates due to overdetection of cardiac events
Solution Approach 1:
The patent extracts and removes problematic intervals from the calculation set. Specifically, it identifies and excludes the longest interval (which likely represents a missed detection or dropout) and the shortest intervals (which likely represent overdetections) from the set of all detected intervals before calculating the average rate. This selective removal of harmful data points improves accuracy without requiring complex algorithms.
Solution Approach 2:
The patent changes the parameter selection criteria by applying weighted averaging where different intervals are assigned different weights based on their likelihood of being accurate. Recent intervals are given higher weights, and intervals suspected of being overdetections or dropouts are given lower or zero weights. This transforms the simple unweighted average into a more sophisticated weighted calculation that improves precision.
2Measurement precision
If overdetection is reduced by excluding certain intervals, then rate accuracy improves, but the risk of detecting true arrhythmias decreases
Solution Approach 1:
The patent implements dynamic adjustment of the detection threshold and interval exclusion criteria based on the observed pattern of detected events. The system adapts to changing cardiac conditions by modifying which intervals are excluded and how weights are assigned, allowing it to maintain high accuracy while preserving sensitivity to true arrhythmias. This dynamic approach prevents false negatives.
Solution Approach 2:
The system uses feedback from the detected interval patterns to continuously refine its rate calculation. By monitoring the distribution of interval lengths and the consistency of detected events, the algorithm adjusts its exclusion criteria and weighting scheme in real-time, ensuring that true arrhythmias are not masked by overly aggressive filtering while still eliminating calculation errors.
Data Source
AI summary
Devices and methods for analyzing cardiac signal data. An illustrative method includes identifying a plurality of detected events and measuring intervals between the detected events for use in rate estimation. In the illustrative embodiment, a set of intervals is used to make the rate estimation by first discarding selected intervals from the set. The remaining intervals are then used to calculate an estimated interval, for example by averaging the remaining intervals.