ECG Beat Reclassification After Cardiac Event Relabeling
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Solution Overview
Problem
Existing cardiac monitoring systems face challenges in accurately classifying cardiac events and associated heartbeats due to initial misclassifications, leading to potential misdiagnosis and inefficient resource utilization.
Innovation Solution
A system and method for reclassifying cardiac events and heartbeats using a machine learning model on a server, allowing users to modify cardiac event classifications in a user interface, which automatically updates beat classifications based on the new event classification, reducing computational and network burdens by local processing on a remote computing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If initial cardiac event classification is performed using automated systems, then processing speed is improved, but classification accuracy deteriorates due to potential misclassification
Solution Approach 1:
The system allows users to review initial automated classifications and provide feedback by correcting misclassified events. This feedback mechanism enables the system to learn from errors and improve future classifications, resolving the contradiction between automated processing speed and accuracy.
Solution Approach 2:
The system performs preliminary automated classification to provide quick initial results, then allows for subsequent manual review and correction. This preliminary action enables fast processing while maintaining the opportunity for accuracy improvement through user feedback.
2Measurement precision
If beat classifications are manually updated after event reclassification, then classification accuracy is improved, but time consumption increases
Solution Approach 1:
The system automatically updates beat classifications based on the reclassified event type without requiring manual intervention for each beat. This self-service mechanism maintains accuracy while minimizing time consumption by eliminating repetitive manual updates.
Solution Approach 2:
The system merges the event reclassification operation with automatic beat classification updates into a single integrated action. This combining of operations achieves accurate classification while reducing time consumption compared to separate manual update steps.
3Loss of information
If comprehensive metadata records are maintained for all classifications, then information completeness is improved, but data management complexity increases
Solution Approach 1:
The system maintains comprehensive metadata records that track the history of classifications and corrections. This feedback-oriented record-keeping ensures information completeness while managing complexity through structured organization of metadata related to classification changes.
Solution Approach 2:
The system preliminarily organizes metadata structures before data collection begins, establishing clear frameworks for storing classification information. This preliminary organization simplifies data management complexity while ensuring complete information records are maintained.
Data Source
AI summary
A method includes receiving, by a first computing system, electrocardiogram (ECG) data and metadata associated with the ECG data. The metadata includes an initial cardiac event classification and an initial beat classification for beats occurring during a first event associated with the initial cardiac event classification. The method further includes displaying the ECG data in a user interface, receiving a command to change the initial cardiac event classification to a subsequent cardiac event classification, and automatically modifying the initial beat classifications to subsequent beat classifications based on the subsequent cardiac event classification.