ECG Heartbeat Classification via Multi-Lead Tensor Fusion
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Solution Overview
Problem
Current automatic ECG analysis methods relying on independent analyses of single leads are prone to classification errors due to their complexity and variability, resulting in insufficient accuracy for clinical analyses.
Innovation Solution
An automatic identification and classification method using artificial intelligence that processes ECG digital signals to generate heart beat time sequence and lead heart beat data, performs filtering and data conversion, and inputs the data into a trained LepuEcgCatNet classification model based on deep convolutional neural networks to enhance accuracy by considering multiple leads and their correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods use independent analysis of single lead for summarizing results and doing statistics, then the analysis process is simple, but classification errors increase and accuracy decreases
Solution Approach 1:
The patent combines multiple lead analyses into a unified classification framework. Instead of independently analyzing each lead and summarizing results, the method integrates signals from multiple leads (including spatial relationships and temporal correlations) into a single comprehensive analysis process, thereby improving classification accuracy while managing complexity through unified processing
Solution Approach 2:
The patent creates a multi-functional analysis system that simultaneously performs multiple tasks: spatial correlation analysis, temporal variation analysis, and classification decision-making. This universal approach allows the system to handle complex multi-lead data while providing comprehensive diagnostic information, resolving the contradiction between accuracy improvement and complexity increase
2Productivity
If automatic analysis software is used to analyze ECG data, then productivity increases, but accuracy is insufficient due to complexity and variability of ECG signals
Solution Approach 1:
The patent replaces traditional rule-based automatic analysis systems with an AI-driven intelligent system. The neural network model learns complex patterns and variations in ECG signals automatically, substituting manual feature engineering and rigid decision rules with adaptive intelligent algorithms that can handle signal complexity and variability while maintaining high processing speed
Solution Approach 2:
The patent transforms the analysis approach by changing from fixed threshold-based parameters to dynamic learned parameters. The AI model adapts its internal parameters based on training data, allowing it to handle the variability of ECG signals effectively. This parameter transformation enables the system to maintain both high productivity and improved accuracy by learning optimal decision boundaries from diverse clinical cases
Data Source
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AI summary
An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data (110); cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data (120); performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array (130); performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data (140); and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information (150). The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.