ECG Pause Episode Classification for False Pause Detection
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Solution Overview
Problem
Existing medical devices struggle to accurately detect cardiac events such as asystole, which are not effectively solve the aforementioned technical problems, such as bradycardia, tachycardia, or asystole, due to noise, artifacts, and signal amplitude variations, which are not effectively address the aforementioned issues within a certain technical area that existing technologies have not addressed or effectively solve the aforementioned technical problems, such as bradycardia, tachycardia, fibrillation, or asystole, e.g., caused by sinus pause or AV block, which is referred to herein as cardiac pause.
Innovation Solution
The medical system employs processing circuitry to analyze cardiac electrogram (EGM) data for false pause detection by evaluating criteria such as relative flatness of amplitude values and noise status during a pause interval, classifying episodes as high, low, or normal priority for review, thereby improving the accuracy of true pause identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If medical devices monitor cardiac EGM to detect arrhythmia, then detection capability is provided, but accuracy deteriorates due to noise, artifacts, and signal amplitude variations
Solution Approach 1:
The system performs preliminary actions by analyzing signal characteristics before final pause determination. Specifically, it evaluates amplitude values, detects noise artifacts, and assesses signal quality in advance to determine whether a detected pause is likely true or false, thereby improving accuracy before clinical action is taken
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring signal quality and using this information to adjust detection sensitivity. The processing circuitry receives feedback about noise levels and amplitude variations to modulate the pause detection algorithm, reducing false positives while maintaining true pause detection capability
2Measurement precision
If pause-triggered episodes are reviewed manually, then thorough evaluation is achieved, but time consumption increases
Solution Approach 1:
The system segments the review process into automated triage and manual evaluation stages. Processing circuitry first segments and categorizes pause-triggered episodes based on detected criteria (amplitude analysis, noise detection), creating priority levels that guide which episodes require manual review and which can be automatically managed, thereby reducing overall time consumption while maintaining thorough evaluation where needed
Solution Approach 2:
The system performs self-service by automatically analyzing and prioritizing pause episodes using built-in processing circuitry. The device autonomously evaluates amplitude values, detects noise artifacts, and assigns priority levels without requiring immediate manual intervention, allowing the system to manage itself and reduce the time burden on clinicians
3Measurement precision
If detection sensitivity is increased to capture all pauses, then true pause identification improves, but false positives increase
Solution Approach 1:
The system applies local quality by analyzing different characteristics of the EGM signal at different locations and time points. It specifically examines amplitude values during pause intervals, detects noise artifacts at specific time points, and evaluates signal quality locally to distinguish true pauses from false positives, thereby improving identification accuracy while reducing false alarms
Data Source
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AI summary
This disclosure is directed to techniques for identifying false detection of pause-triggered episode in cardiac ECG data. In some examples, a medical system is configured to receive a cardiac electrogram of a pause-triggered episode, the cardiac electrogram sensed by a medical device via a plurality of electrodes, determine whether one or more of false pause detection criteria are satisfied based on the cardiac electrogram, wherein the one or more of false pause detection criteria comprise: at least one criterion for relative flatness of amplitude values of the cardiac electrogram in a time interval between a last pre-pause beat and a pause detection time, classify the pause-triggered episode as one of a plurality of classifications based on the determination of whether the false pause detection criterion is satisfied, and output an indication of the classification of the pause-triggered episode to a user display.