ECG Analysis Neural Network Brugada Syndrome Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current electrocardiogram (ECG) analysis systems are inefficient in diagnosing Brugada Syndrome, as they often require expert interpretation and may not detect subtle abnormalities, leading to inconsistent and potentially unsafe diagnoses, especially since many patients show normal ECGs, and existing methods require dangerous drugs to induce ECG abnormalities for diagnosis.
Innovation Solution
A computer-implemented method that uses neural networks to analyze ECG traces by filtering out abnormal segments through principal component analysis and Hotelling T^2 statistic, generating representative traces without the need for drugs, thereby improving diagnostic accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert interpretation is used for ECG analysis, then diagnostic accuracy may be improved, but reliability and consistency deteriorate due to limited access and human judgment variability
Solution Approach 1:
The patent replaces the mechanical system of expert human interpretation with an automated computer-based analysis system that processes ECG traces. This substitution eliminates human judgment variability and limited access to experts, providing consistent and reliable diagnoses through automated algorithms while maintaining diagnostic accuracy.
2Device complexity
If raw ECG traces are displayed for diagnosis, then simplicity is maintained, but measurement precision deteriorates as subtle abnormalities become undetectable
Solution Approach 1:
The system performs preliminary processing of raw ECG traces before presentation to the user. It automatically identifies, filters, and selects diagnostically relevant trace segments, enhancing subtle abnormalities and preparing optimized visualizations. This preliminary action improves detectability of subtle patterns while maintaining interface simplicity.
Solution Approach 2:
The system applies parameter changes to ECG traces including filtering, scaling, and enhancement of specific waveform characteristics. By modifying trace parameters to highlight diagnostically relevant features and suppress noise, the system improves detection of subtle abnormalities without requiring complex user interaction.
3Measurement precision
If drug challenge tests are administered to unmask ECG abnormalities, then diagnostic accuracy improves, but harmful factors increase due to pro-arrhythmic effects
Solution Approach 1:
The patent introduces an intermediary computer-based analysis system that detects subtle ECG abnormalities without requiring drug administration. This intermediary system uses advanced signal processing and pattern recognition to identify diagnostic patterns in baseline ECG traces, eliminating the need for harmful drug challenge tests while maintaining diagnostic accuracy.
4Measurement precision
If multiple ECG trace segments are analyzed, then measurement precision improves, but device complexity increases due to processing requirements
Solution Approach 1:
The system segments ECG traces into multiple discrete segments for individual analysis. By dividing the continuous ECG signal into manageable segments, the system can apply sophisticated processing to each segment while maintaining overall computational efficiency. This segmentation enables precise feature detection without overwhelming processing complexity.
Solution Approach 2:
The system analyzes multiple ECG trace segments, performing more analysis than a single trace would provide. By processing multiple segments and synthesizing results, the system achieves superior diagnostic accuracy through cumulative information while managing complexity through efficient algorithms.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A computer-implemented method of facilitating electrocardiogram ("ECG") analysis involves receiving one or more sensed ECG traces for a patient, each of the sensed ECG traces representing sensed patient heart activity over a sensed time period, and, for each of the one or more sensed ECG traces: identifying a plurality of corresponding sensed ECG trace segments, each of the sensed ECG trace segments representing sensed patient heart activity for the patient over a segment of the sensed time period, and determining a representative ECG trace based on at least one of the identified corresponding sensed ECG trace segments. The method involves causing at least one neural network classifier to be applied to the one or more determined representative ECG traces to determine one or more diagnostically relevant scores related to at least one diagnosis of the patient. Other methods, systems, and computer readable media are disclosed.