ECG Data Processing Server with Expected Analysis Time Prediction
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Solution Overview
Problem
The existing methods for analyzing electrocardiogram (ECG) signals are time-consuming, often requiring several hours for a 24-hour measurement, and there is a need to predict the time required for analysis to improve efficiency and manage expectations.
Innovation Solution
A method is developed that includes a server for processing ECG data, which receives input from a user terminal, generates data combining the ECG signal with an expected analysis time, and transmits this data back to the user terminal for display. This method uses previously analyzed ECG data and classification information to calculate the expected analysis time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of electrocardiogram signals is performed, then analysis accuracy is maintained, but analysis time increases significantly (3-6 hours for 24-hour measurement)
Solution Approach 1:
The system performs preliminary classification of electrocardiogram signals into normal and abnormal categories before detailed manual analysis. This preliminary action filters out normal signals from further analysis, allowing analysts to focus only on abnormal signals that require detailed review, thereby reducing overall analysis time while maintaining accuracy.
Solution Approach 2:
The analysis process is segmented into multiple stages: automated preliminary classification, intermediate review of abnormal signals, and final detailed analysis. This segmentation allows the system to handle large volumes of data efficiently by processing different portions of the data through different methods at different stages.
2Measurement precision
If analysis time is extended to improve accuracy, then measurement precision improves, but productivity decreases
Solution Approach 1:
Automated preliminary classification is performed on all electrocardiogram signals to identify abnormal ones before they are sent to analysts. This preliminary action reduces the volume of data requiring detailed human analysis, thereby increasing productivity while maintaining precision through targeted manual review of only the necessary abnormal signals.
Solution Approach 2:
The system extracts and separates normal signals from abnormal signals through automated classification. By taking out normal signals from the analysis pipeline, the system eliminates unnecessary detailed analysis of these signals, thereby increasing the throughput of abnormal signal analysis without compromising precision.
3Ease of operation
If expected analysis time is predicted to manage expectations, then communication effectiveness improves, but prediction accuracy may vary
Solution Approach 1:
The system provides feedback to users about expected analysis times based on the characteristics of the electrocardiogram signals. This feedback mechanism allows users to manage their expectations about when results will be available, improving communication effectiveness while the system continuously refines prediction accuracy based on actual analysis times.
Data Source
AI summary
A method of displaying analysis data of an electrocardiogram signal includes receiving, by an electrocardiogram data processing server, a load input with respect to a first electrocardiogram signal from a user terminal, generating, by the electrocardiogram data processing server, data including analysis data of the first electrocardiogram signal and a first expected analysis time of the first electrocardiogram signal, and transmitting, by the electrocardiogram data processing server, the analysis data to the user terminal to display, on the user terminal, a first window displaying the first electrocardiogram signal and a second window displaying the first expected analysis time.