ECG Cardiac Mapping for Early Abnormality Classification
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Solution Overview
Problem
Existing cardiac diagnostic methods, such as cardiac catheterization, nuclear photography, and cardiac ultrasound, are invasive, costly, and require specialized equipment and personnel, while electrocardiograms (ECGs) lack effectiveness in detecting atypical myocardial infarction or providing early symptoms.
Innovation Solution
A cardiac diagnostic method using electrocardiogram signals to classify abnormal heart positions and degrees of abnormalities through a virtual heart model, machine learning, and a mapping tensor to generate cardiac potential vectors, reducing the need for invasive procedures and specialized personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cardiac catheterization is used for diagnosis, then detection precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual heart model that copies the anatomical and electrical characteristics of a real heart. This virtual model allows simulation and analysis of cardiac conditions without requiring physical invasive procedures or expensive specialized equipment, thus reducing device complexity while maintaining detection precision through computational modeling
Solution Approach 2:
The patent replaces mechanical invasive procedures (cardiac catheterization) with computational methods. By using electrocardiogram signals and processing them through algorithms against the virtual heart model, the system achieves diagnosis without mechanical intervention, reducing both device complexity and procedural invasiveness
2Measurement precision
If cardiac catheterization is used for diagnosis, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The virtual heart model is pre-created with comprehensive anatomical and electrical data before actual diagnosis is needed. This preliminary preparation allows rapid analysis of patient ECG data against the pre-computed model, eliminating the need for time-consuming real-time procedural setup and reducing overall diagnosis time while maintaining precision
Solution Approach 2:
By substituting mechanical procedures with computational analysis, the patent eliminates procedural setup time, instrument preparation time, and recovery time associated with invasive methods. The diagnostic process becomes a matter of data input and algorithm processing, significantly reducing time loss
3Ease of operation
If electrocardiogram is used for routine detection, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent enhances the simplicity of ECG-based detection by creating a virtual heart model that serves as a reference copy. This allows straightforward comparison of patient ECG data against the virtual model using automated algorithms, maintaining ease of operation while improving detection precision through sophisticated computational matching
Solution Approach 2:
The patent replaces manual interpretation of ECG data by professionals with automated computational analysis. By processing ECG signals through algorithms that compare them against the virtual heart model, the system maintains the simplicity and accessibility of ECG testing while significantly improving detection precision for atypical and early-stage conditions
4Measurement precision
If specialized equipment and personnel are required for cardiac diagnosis, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The virtual heart model serves as a digital copy that encapsulates complex cardiac anatomy and physiology. This allows the system to perform sophisticated diagnostic functions that would traditionally require expensive specialized equipment and highly trained personnel, while using only standard ECG machinery and computational algorithms
Solution Approach 2:
The patent substitutes mechanical and human expertise with computational systems. By using algorithms to process ECG data against the virtual model, the system achieves specialized diagnostic capabilities without requiring expensive specialized equipment or extensive professional training, thereby reducing device complexity and cost while maintaining precision
Data Source
AI summary
A cardiac diagnostic method includes the following steps: providing an electrocardiogram (ECG) signal; obtaining a body surface potential vector according to the ECG signal; calculating the body surface potential vector and a pre-created mapping tensor to obtain a cardiac potential vector, wherein the mapping tensor is obtained according to a plurality of abnormal positions and a plurality of degree of abnormalities generated by a reference heart; and classifying the abnormal positions and the degree of abnormalities of a detected heart corresponding to the ECG signal according to the cardiac potential vector. In addition, the present disclosure further provides a non-transitory computer-readable medium and system that can operate the above-mentioned cardiac diagnostic method.