Automated Post-Processing for Cardiac Event Classification
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Solution Overview
Problem
The increasing data review time for implantable medical devices like ICDs, due to rising episode detection numbers and decreasing available time for post-processing, leads to a shortage of clinicians with the necessary expertise, resulting in potential misclassification of ventricular arrhythmias and inefficient management of patients.
Innovation Solution
A method and apparatus for automatically classifying cardiac events by analyzing data from implantable medical devices, using techniques such as A/V ratio determination, abrupt onset analysis, and template generation to accurately reclassify episodes, thereby reducing clinician review time and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of episode detections by ICD increases, then the diagnostic capability and monitoring coverage improve, but the time required for clinician review increases significantly
Solution Approach 1:
An automated post-processing system acts as an intermediary between the ICD device and the clinician. This system automatically retrieves stored episodes from the ICD, classifies them using multiple algorithms (morphology analysis, A/V ratio determination, abrupt onset detection), and presents only the most relevant episodes to the clinician, thereby filtering out false alarms and reducing review time while maintaining detection accuracy
Solution Approach 2:
The manual mechanical review process by clinicians is replaced with an automated computational system that uses electronic algorithms to analyze episode data. The system employs template matching, signal processing, and classification algorithms to automatically evaluate episodes, substituting the manual mechanical review with an automated electronic assessment process
2Measurement precision
If clinicians manually review all detected episodes with expert knowledge, then classification accuracy improves, but the availability of expert clinicians decreases as the patient population increases
Solution Approach 1:
The system enables self-service automated classification of episodes using multiple independent algorithms that evaluate different aspects of the episode data (morphology, A/V ratio, abrupt onset). These algorithms work autonomously to classify episodes without requiring continuous expert clinician intervention, allowing the system to process episodes independently and present only uncertain or complex cases for human review
Solution Approach 2:
The post-processing system performs multiple functions within a single integrated platform: it retrieves data from the ICD, applies multiple classification algorithms simultaneously (morphology analysis, A/V ratio determination, abrupt onset detection), generates comprehensive reports, and assists clinicians in decision-making. This multi-functional system replaces the need for multiple specialized review processes
3Loss of time
If automated algorithms are used to classify episodes, then clinician review time decreases, but the risk of misclassification increases without expert oversight
Solution Approach 1:
The classification process is segmented into multiple independent algorithmic components, each evaluating different characteristics of the episode (morphology features, A/V ratio metrics, abrupt onset patterns). By dividing the classification task into separate analytical segments rather than relying on a single algorithm, the system achieves more reliable and comprehensive episode characterization
Solution Approach 2:
The system incorporates feedback mechanisms where classification results from multiple algorithms are integrated and evaluated. The post-processing system provides feedback to clinicians about the confidence levels of automated classifications and presents episodes that require human expert review, creating a feedback loop that maintains reliability while reducing routine review time
Data Source
AI summary
A method and system for post-processing of sensing data associated with identification of a cardiac event by a medical device that includes transmitting a plurality of stored sensing data generated by the medical device to an access device, the stored sensing data including sensed atrial events and sensed ventricular events. The access device determines, in response to the transmitted data, instances where the medical device identified a cardiac event being detected in response to the sensing data, and generates a template in response to correlated morphologies of adjacent intervals prior to a detection interval corresponding to the cardiac being identified as the cardiac event and a morphology of the detection interval.