ECG Reading Correction Using Waveform Grouping and Template Matching
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Solution Overview
Problem
Existing electrocardiogram reading systems face challenges with inaccurate automatic reading functions, particularly in real-time applications, and require manual correction due to the complexity and diversity of waveforms, leading to inefficiencies and prolonged analysis times.
Innovation Solution
An electrocardiogram reading apparatus with components like an electrocardiogram waveform acquisition unit, waveform reading unit, occurrence time management unit, grouping unit, and reading correction unit, which allows for collective correction of similar waveforms by clustering and correlation analysis to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rule-based algorithms are used for arrhythmia detection, then the system can operate with simple processing, but the accuracy deteriorates due to waveform diversity
Solution Approach 1:
The patent uses template waveforms as reference copies to compare against actual ECG waveforms. Multiple template waveforms representing different arrhythmia types are stored and matched against incoming signals, enabling accurate detection without complex rule-based algorithms. The template matching approach copies ideal waveform patterns and uses correlation analysis to identify matches.
Solution Approach 2:
The system changes the approach from rule-based detection to parameter-based template matching. By transforming the problem into comparing waveform parameters (amplitude, duration, shape characteristics) against stored templates, the system achieves higher accuracy while maintaining computational efficiency.
2Measurement precision
If deep learning algorithms are used for electrocardiogram analysis, then the accuracy improves, but the processing speed decreases making real-time operation difficult
Solution Approach 1:
The patent segments the ECG waveform into distinct components (P wave, QRS complex, T wave) and analyzes each segment separately using template matching. This segmentation allows for faster processing compared to analyzing the entire waveform with deep learning, while maintaining accuracy through focused comparison of critical segments against corresponding template portions.
Solution Approach 2:
The system extracts key features and critical segments from the ECG waveform for analysis, rather than processing the entire signal. By taking out only the essential waveform portions for template comparison, the processing speed increases while retaining the accuracy needed for clinical diagnosis.
3Measurement precision
If manual correction of each reading is performed, then the accuracy improves, but the time consumption increases significantly
Solution Approach 1:
The patent merges multiple waveform readings that share similar characteristics into groups. When one reading in a group is corrected, the correction is automatically applied to all similar readings in that group. This combining approach maintains high accuracy while dramatically reducing the time needed for manual correction by eliminating redundant work.
Solution Approach 2:
The system implements feedback mechanisms where corrections made to one reading are fed back to automatically update similar readings. The correction information flows back through the system, allowing automatic propagation of corrections across grouped waveforms, reducing manual intervention time while maintaining accuracy.
4Measurement precision
If the entire electrocardiogram waveform is reviewed for reading verification, then the accuracy improves, but the complexity and time required increase
Solution Approach 1:
The patent applies local quality verification by focusing analysis on specific critical portions of the waveform rather than reviewing the entire signal. Different verification strategies are applied to different segments (P wave morphology, QRS duration, T wave characteristics), reducing overall complexity while maintaining comprehensive accuracy through targeted local verification.
Data Source
AI summary
Disclosed are method and apparatus for correcting an electrocardiogram reading.When using an electrocardiograph in the present embodiment, there is a possibility that the automatic reading function incorrectly reads the patient's electrocardiogram. Therefore, since rereading is necessary with reference to the results of the automatic reader, a method and apparatus for correcting an electrocardiogram reading, which allow the electrocardiogram reading to be easily corrected and then checked when rereading the automatic reading result, are provided.