Multi-Threshold Cardiac Event Detection Algorithm
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Solution Overview
Problem
Reliable detection of R waves in cardiac events is challenging due to varying baseline and morphology, leading to poor quality of data and reduced specificity in co-morbidity detection, especially with fixed threshold methods.
Innovation Solution
A method involving multithreshold retrospective analysis of electrograms, where the electrogram is applied to an event detector with spaced-apart thresholds, determining characteristic features like slope, amplitude, or threshold crossing time, and comparing these to a template to accurately identify the time of cardiac events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed threshold R wave detection is used, then the device complexity is reduced, but the measurement precision of R wave detection deteriorates under varying baseline and morphology conditions
Solution Approach 1:
The detection algorithm is segmented into multiple independent threshold levels (first threshold, second threshold, third threshold) rather than using a single fixed threshold. Each threshold operates independently to detect different characteristics of the R wave, allowing the system to maintain simplicity while improving precision through multi-level analysis.
Solution Approach 2:
The system dynamically adjusts the threshold values based on the electrogram characteristics. The first, second, and third thresholds are selectively applied depending on the detected signal properties, enabling the fixed threshold structure to adapt to varying baseline and morphology conditions without requiring complex real-time adjustment mechanisms.
2Ease of operation
If single threshold detection is used, then the ease of operation is maintained, but the reliability of R wave detection deteriorates under varying conditions
Solution Approach 1:
The detection system is segmented into multiple threshold levels that independently evaluate different aspects of the electrogram. This segmentation allows the system to maintain operational simplicity while improving reliability by cross-validating detections across multiple threshold levels, ensuring consistent R wave identification under varying conditions.
Solution Approach 2:
The system incorporates feedback mechanisms where the detection results from different threshold levels are evaluated together. The microprocessor analyzes the relationships between the first, second, and third threshold crossings to confirm R wave detections, providing feedback that enhances reliability without complicating the overall operation of the device.
3Device complexity
If fixed threshold methods are used, then the device complexity is minimized, but the measurement precision of cardiac interval determination deteriorates
Solution Approach 1:
The cardiac interval measurement process is segmented into multiple detection stages using different threshold levels. By measuring R wave positions through the first, second, and third thresholds and then calculating intervals between these segmented detection points, the system achieves precise cardiac interval measurement while maintaining relatively simple processing algorithms.
Solution Approach 2:
The system performs preliminary threshold crossings to establish reference points for R wave detection before calculating cardiac intervals. The first, second, and third thresholds are used to preliminarily identify R wave positions, and only after these preliminary detections are made does the system proceed to calculate the QT interval and other cardiac parameters, ensuring precision without complex real-time processing.
Data Source
AI summary
A system and method provide precise detection of the time of occurrence of a cardiac event of a heart. The method comprises the steps of sensing electrical activity of the heart to generate an electrogram of the heart and applying the electrogram to an event detector having a plurality of spaced apart thresholds. The thresholds are selected such that the electrogram has an amplitude for crossing at least one of the thresholds. The method further comprises determining a characteristic identifying feature of the electrogram at each threshold crossing of the electrogram, comparing the determined characteristic identifying features to an electrogram template, and identifying the time of occurrence of the cardiac event based upon the comparison.