ECG Analysis Server Using Signal Segmentation and Time Prediction
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Solution Overview
Problem
The existing methods for analyzing electrocardiogram data are time-consuming, often taking 3 to 6 hours, and the analysis time can vary, leading to unpredictable fees and inefficiencies in medical diagnosis.
Innovation Solution
An electrocardiogram data processing server that calculates expected analysis time by analyzing electrocardiogram signals using machine learning algorithms, considering past medical history and symptom information, and adjusts analysis conditions based on input from medical staff or analysts to optimize the analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual analysis methods are used for electrocardiogram data, then comprehensive analysis can be performed, but the analysis time becomes excessively long (3-6 hours) and unpredictable
Solution Approach 1:
The patent segments the electrocardiogram analysis process into multiple stages: automated pre-processing by the server, identification of sections of interest, and focused manual analysis by analysts. This segmentation reduces the overall analysis time while maintaining comprehensive analysis quality by dividing the workload between automated systems and human experts.
Solution Approach 2:
The server performs preliminary automated analysis before the analyst's manual review. This preliminary action includes signal processing, noise filtering, and identification of potential abnormalities, which prepares the data in advance and allows the analyst to focus only on critical sections, thereby reducing total analysis time while maintaining comprehensive evaluation.
2Measurement precision
If the electrocardiogram measurement period is extended to capture more data, then diagnostic accuracy improves, but the analysis time increases proportionally
Solution Approach 1:
The server extracts and identifies only the relevant sections of interest from the extended electrocardiogram measurement data. Instead of requiring analysts to review the entire extended dataset, the system automatically extracts critical segments that contain diagnostic information, thereby maintaining diagnostic accuracy while significantly reducing the time required for manual analysis.
Solution Approach 2:
The system performs automated analysis on the entire extended dataset to identify all potential abnormalities, then presents only the necessary portions to the analyst. This partial manual review approach maintains diagnostic accuracy by ensuring all critical areas are captured, while reducing analysis time by eliminating redundant manual review of normal segments.
3Productivity
If automated processing is increased to reduce analysis time, then efficiency improves, but the ability to handle complex cases and provide accurate diagnosis may be compromised
Solution Approach 1:
The system implements feedback loops where the server's automated analysis results are reviewed and validated by analysts, and this feedback is used to continuously improve the automated processing algorithms. This feedback mechanism ensures that automated processing maintains high reliability by learning from expert corrections while preserving analysis efficiency through iterative optimization.
Solution Approach 2:
The server acts as an intermediary between the raw electrocardiogram data and the analyst's final diagnosis. It performs automated preprocessing and identification of sections of interest, but the analyst retains authority over the final diagnostic interpretation. This intermediary role allows automated processing to improve efficiency while human expertise ensures diagnostic accuracy and reliability.
Data Source
AI summary
Disclosed is a method of generating and processing analysis data regarding electrocardiogram signals of a target object. The method includes receiving an electrocardiogram signal of a target object and a first classification data regarding the electrocardiogram signal of the target object. The method further includes calculating statistical data in consideration of a past medical history and symptom information at time of measurement of the target object, generating a second classification data by applying the statistical data to the electrocardiogram signal of the target object, determining a section of interest to be analyzed in consideration of the electrocardiogram signal and the second classification data, extracting signal sections corresponding to the section of interest to be analyzed from the second classification data regarding the electrocardiogram signal, calculating an expected analysis time for the signal sections, and transmitting analysis data regarding the signal sections to an analyst terminal.