Brady Pause Detection in Implantable Cardiac Monitors
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Solution Overview
Problem
Conventional implantable loop recorders (ILRs) and insertable cardiac monitors (ICMs) have a high rate of false bradycardia pause detection due to under-sensing of R-waves and dynamic threshold complications, leading to unnecessary clinician review time.
Innovation Solution
A cardiac signal sensing circuit and pause detection circuit are implemented to identify ventricular depolarization, detect candidate pause episodes, and discard signals with excessive noise events, using a two-tiered approach to confirm bradycardia pauses by analyzing signal-to-noise metrics and rejecting false episodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sensing circuits and dynamic threshold detection are used, then the device can detect cardiac events, but the rate of false bradycardia pause detection increases due to under-sensing of R-waves and noise events
Solution Approach 1:
The detection process is segmented into two distinct tiers: a first tier that identifies candidate pause episodes based on RR intervals, and a second tier that validates these candidates by analyzing signal-to-noise metrics. This segmentation allows the system to separate initial detection from confirmation, reducing false positives while maintaining sensitivity to true pauses.
Solution Approach 2:
The system implements feedback by using the detected pause episode information to adjust detection parameters and thresholds. The validation tier provides feedback on the quality of detected pauses, allowing the system to refine its detection algorithm and reduce false positives in subsequent detections.
2Measurement precision
If dynamic threshold detection is used to adapt to varying signal conditions, then detection sensitivity improves, but false detections increase due to threshold complications and noise
Solution Approach 1:
The detection algorithm is divided into candidate identification and validation stages. The first tier uses dynamic threshold detection to identify potential pauses, while the second tier applies additional criteria including signal-to-noise ratio analysis to validate these candidates. This segmentation allows dynamic thresholds to maintain sensitivity while the validation stage eliminates false alarms.
Solution Approach 2:
The system changes detection parameters based on signal characteristics. Instead of using a fixed threshold, the system adapts thresholds based on the detected signal quality and noise levels. Parameter changes are implemented dynamically during the two-tiered detection process to optimize both sensitivity and specificity.
3Reliability
If all detected pause episodes are recorded for review, then no true pauses are missed, but clinician review time increases unnecessarily due to false positives
Solution Approach 1:
The recording process is segmented into candidate episodes and validated episodes. Only pause episodes that pass both the first-tier candidate identification and the second-tier validation are recorded for clinician review. This segmentation ensures that no true pauses are missed while significantly reducing the number of false positives requiring review.
Solution Approach 2:
The system performs preliminary validation of detected pause episodes before they are recorded for clinician review. The second tier of detection acts as a preliminary filter that validates candidates based on multiple criteria including signal-to-noise ratios and morphological features. This preliminary action ensures that only high-confidence pause episodes are recorded, saving clinician time while maintaining completeness.
Data Source
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AI summary
An apparatus includes a cardiac signal sensing circuit configured to generate a sensed cardiac signal representative of electrical cardiac activity of a subject, a buffer memory and a pause detection circuit. The pause detection circuit is configured to: identify ventricular depolarization in the cardiac signal or the sampled cardiac signal; detect a candidate pause episode using the cardiac signal in which delay in ventricular depolarization exceeds a specified delay threshold; identify noise events in a stored cardiac signal; and discard the cardiac signal of the candidate pause episode when a number of noise events satisfies a specified noise event number threshold, otherwise store the cardiac signal of the candidate pause episode as a bradycardia pause episode.