ECG Analysis Model Segmentation for Diagnostic Accuracy
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Solution Overview
Problem
Current electrocardiogram analysis methods lack accuracy and efficiency in diagnosing heart diseases, particularly in non-hospital settings, due to limitations in data acquisition and analysis techniques, which can lead to false positives and false negatives.
Innovation Solution
A method and apparatus for electrocardiogram analysis that involves acquiring and segmenting electrocardiogram data, inputting it into a pre-trained model to generate heart disease suffering probability vectors, and using these vectors to determine diagnosis results, with options for different application scenarios and threshold settings to minimize false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrocardiogram analysis methods are used, then the analysis process is simple, but the diagnostic accuracy is low and false positives/negatives occur frequently
Solution Approach 1:
The electrocardiogram data is segmented into multiple data segments, and each segment is independently input into the analysis model. This segmentation allows the system to process complex patterns in different time windows, improving diagnostic accuracy by capturing transient cardiac conditions that would be missed in continuous analysis.
Solution Approach 2:
The electrocardiogram data undergoes preliminary processing steps including segmentation and feature extraction before being input into the analysis model. This preliminary action prepares the data in an optimized format, enabling the model to achieve higher diagnostic accuracy without requiring overly complex processing during the actual analysis phase.
2Adaptability or versatility
If more comprehensive heart disease types are analyzed, then the diagnostic coverage is improved, but the analysis time and computational complexity increase
Solution Approach 1:
The analysis is divided into multiple independent segments, each processed by the model to generate probability vectors for different heart disease types. This segmentation enables parallel processing of multiple disease classifications simultaneously, maintaining comprehensive diagnostic coverage while reducing total analysis time through concurrent computation.
Solution Approach 2:
The system uses probability vectors with K components to represent the likelihood of K different heart disease types, where K can be dynamically adjusted based on the specific diagnostic needs. This parameterization allows the system to adaptively cover comprehensive disease types while controlling computational complexity by only processing the relevant K diseases for each analysis case.
Data Source
AI summary
An electrocardiogram analysis method and apparatus, an electronic device and a storage medium are provided. In the method, at least one electrocardiogram data segment to be analyzed of a target user is input into a first electrocardiogram analysis model for analysis, so as to generate heart disease diagnosis result information of the target user; and optionally, when the heart disease diagnosis result information indicates that the probability of the target user suffering from a specific heart disease is relatively low, that is, when the electrocardiogram data segment to be analyzed is an electrocardiogram that looks relatively normal, whether the target user suffers in a paroxysmal manner from the heart disease is further determined. That is, the probability of the target user having the symptom corresponding to the heart disease in the future is predicted to provide early warning information for a future physical health condition of the target user.