Multi-group ECG Analysis Framework for Arrhythmia Detection
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Solution Overview
Problem
Current ECG analysis methods relying on single-lead signals fail to provide comprehensive information for heart beats and do not consider the geometry properties of electrodes and leads, limiting their ability to distinguish multiple types of heart arrhythmias effectively.
Innovation Solution
A multi-group electrocardiography (MG-ECG) analysis framework that groups ECG data from multiple leads using a grouping module and applies axis-specific feature extraction modules, followed by a finely-tuned analysis model to generate feature vectors for comprehensive ECG analysis, including monitoring and computer-aided diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If single-lead ECG signals are used for analysis, then the device complexity is reduced and ease of operation is improved, but the measurement precision and reliability of heart arrhythmia detection deteriorate due to insufficient comprehensive information
Solution Approach 1:
The patent divides multi-lead ECG data into multiple groups (e.g., first group with leads I and II, second group with leads III and aVF, third group with leads aVR and aVL) and applies separate axis-specific feature extraction modules to each group. This segmentation allows comprehensive analysis of multiple leads while maintaining manageable processing through structured grouping, thereby improving measurement precision without excessively increasing operational complexity.
Solution Approach 2:
The patent introduces axis-specific feature extraction that transforms standard ECG leads into different analytical dimensions (e.g., converting leads I and II into P-wave axis features, QRS axis features, T-wave axis features). This dimensional transformation enables comprehensive arrhythmia detection by analyzing ECG data from multiple geometric perspectives, improving measurement precision while keeping the system organized and operable.
2Reliability
If multi-lead ECG data is processed with comprehensive feature extraction, then the reliability and measurement precision of arrhythmia detection is improved, but the device complexity increases due to multiple processing modules
Solution Approach 1:
The patent segments the complex multi-lead ECG processing task into distinct groups, where each group is handled by a dedicated axis-specific feature extraction module. For example, the first module processes leads I and II for horizontal axis features, the second module processes leads III and aVF for inferior axis features, and the third module processes leads aVR and aVL for superior axis features. This segmentation improves reliability through comprehensive analysis while controlling complexity through modular organization.
Solution Approach 2:
The patent creates universal axis-specific feature extraction modules that can handle multiple leads within each group using the same processing logic. Each module is designed to extract multiple types of features (P-wave, QRS, T-wave) from its assigned leads, making the modules multi-functional and adaptable. This universality improves reliability by ensuring consistent analysis across all leads while reducing overall system complexity through reusable components.
3Measurement precision
If axis-specific feature extraction modules are applied to multiple lead groups, then the measurement precision for distinguishing heart arrhythmias is improved, but the processing time and complexity of the analysis system increases
Solution Approach 1:
The patent divides the ECG analysis into parallel processing streams, where each axis-specific feature extraction module independently processes its assigned lead group simultaneously. This segmentation enables concurrent execution of multiple feature extraction tasks, improving measurement precision through comprehensive analysis while minimizing processing time loss by avoiding sequential bottlenecks.
Solution Approach 2:
The patent performs preliminary grouping of ECG leads into logical clusters based on their anatomical and electrical relationships before feature extraction. By pre-organizing leads into groups (e.g., horizontal axis group, inferior axis group, superior axis group) and preparing the processing structure in advance, the system optimizes the subsequent analysis workflow, reducing processing time while maintaining high measurement precision through structured multi-lead analysis.
Data Source
AI summary
A method of performing electrocardiography (ECG) analysis by at least one processor, the method including receiving ECG data that is from multiple leads; grouping the ECG data into groups of data; generating, from each group of the groups of data, a feature vector using a respective machine learning model; and performing ECG analysis using the feature vectors generated from each of the groups of data.