ECG Event Triage Interface for Selective Context Streaming
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Solution Overview
Problem
Existing cardiac monitoring systems face challenges in efficiently managing and analyzing large volumes of electrocardiogram (ECG) data due to limited computing resources and network bandwidth, making it difficult to access and analyze preceding and following ECG data for improved cardiac event analysis.
Innovation Solution
A system that includes a cardiac event server with machine learning models to classify cardiac events, a cardiac event router to prioritize and route data, and a critical event platform that selectively provides access to ECG data and metadata based on user interaction, allowing efficient display and analysis of relevant ECG data subsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all ECG data is transmitted and stored for comprehensive analysis, then complete cardiac event analysis is achieved, but network bandwidth and computing resources are excessively consumed
Solution Approach 1:
The patent extracts and transmits only the most critical information (arrhythmia events and their immediate context) from the complete ECG dataset. The monitoring device identifies specific arrhythmia events and transmits only the ECG data surrounding these events rather than the entire continuous ECG stream, thereby reducing data transmission volume while preserving essential diagnostic information.
Solution Approach 2:
The patent segments the continuous ECG data into discrete event-based subsets. Each subset contains ECG data from a specific time window around a detected arrhythmia event, separating critical event data from routine monitoring data. This segmentation allows selective transmission and processing of only relevant portions of the ECG dataset.
2Ease of operation
If a fixed time window around each arrhythmia event is transmitted, then data transmission is simplified, but preceding and following ECG context is lost
Solution Approach 1:
The patent implements a dynamic data transmission approach where the time window for ECG data transmission is not fixed but adapts based on user interaction. Initially, a first time window around each arrhythmia event is transmitted. When users indicate interest in specific events through the graphical interface, additional time windows extending further back in time are dynamically transmitted to provide broader contextual information.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with the graphical interface (such as selecting specific arrhythmia events for review) trigger additional data transmissions. The system monitors user behavior and dynamically adjusts data transmission based on this feedback, extending time windows to include more preceding and following ECG data when users demonstrate interest in comprehensive analysis of specific events.
Data Source
AI summary
A method includes detecting a first event within electrocardiogram (ECG) data, transmitting a first subset of the ECG data from a first computing system to a second computing system, displaying the first subset of the ECG data in a first window of a user interface (UI) of the second computing system, and selectively transmitting additional subsets of the ECG data from the first computing system to the second computing system as a user interacts with the UI.