12-Lead ECG CNN Feature Extraction for Myocarditis Detection
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Solution Overview
Problem
Classification of medical scan data is hampered by an overabundance of potential parameters, making it difficult for models to converge sufficiently for accurate diagnostics or detection of cardiovascular diseases.
Innovation Solution
A 12-lead electrocardiograph system utilizing a convolutional neural network (CNN) for multi-dimensional convolution operations on voltage-time data to detect cardiovascular diseases, integrating with a classification model for disease indication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification models are used to analyze medical scan data with numerous parameters, then comprehensive disease detection is possible, but model convergence becomes difficult and computational resources are excessively consumed
Solution Approach 1:
The patent segments the complex classification task by introducing intermediate representation layers that decompose the analysis of multi-lead ECG data into manageable components. Each lead's voltage-time data is processed through separate convolutional operations before being integrated, breaking down the overwhelming parameter space into structured feature sets that converge more efficiently
Solution Approach 2:
The patent transforms the data from traditional 2D voltage-time representation across multiple leads into a 3D tensor structure that enables multi-dimensional convolution operations. This dimensional transformation allows the model to simultaneously capture temporal patterns, spatial relationships between leads, and feature interactions, improving convergence while maintaining comprehensive disease detection capability
2Measurement precision
If comprehensive voltage-time data from all 12 leads is processed to improve detection accuracy, then disease indication precision increases, but computational and storage resource consumption increases
Solution Approach 1:
The patent extracts only the most diagnostically relevant features from the comprehensive 12-lead ECG data through specialized convolutional filters. Instead of processing all raw parameters equally, the model selectively extracts meaningful patterns such as QRS complex morphology, ST-segment deviations, and T-wave abnormalities, reducing computational load while preserving detection accuracy
Solution Approach 2:
The patent applies different processing strategies to different regions of the ECG data based on their diagnostic importance. Critical segments like the QRS complex and ST segment receive more sophisticated convolutional analysis, while less informative regions use simplified processing, optimizing the balance between accuracy and computational efficiency
Data Source
AI summary
An apparatus and method for detecting a level of cardiovascular disease. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.