ECG Analysis Neural Network Segmentation for Hidden Wave Detection
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Solution Overview
Problem
Current ECG analysis systems face challenges in accurately and efficiently processing ECG data, particularly in delineation, classification, and multi-label classification, often requiring handcrafted features and failing to identify hidden P-waves and contextual information across multiple beats.
Innovation Solution
A system utilizing machine learning algorithms, including neural networks for delineation and classification, that processes ECG data to identify waves and abnormalities without the need for feature extraction, and groups beats based on similarity, enabling accurate and efficient analysis and visualization of ECG data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms with neural networks are used for ECG delineation and classification, then accuracy and efficiency in identifying abnormalities and hidden P-waves is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent divides the ECG analysis system into separate specialized neural networks: a delineation neural network for wave identification, a classification neural network for abnormality detection, and a grouping neural network for beat classification. This segmentation allows each network to be optimized for its specific function, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary processing layer that extracts features from raw ECG signals and prepares them for input to the neural networks. This intermediary layer acts as a bridge between the raw data and the complex ML models, reducing the computational burden on the neural networks while maintaining high accuracy in abnormality detection.
2Productivity
If automated machine learning analysis is implemented, then productivity and speed of ECG interpretation is improved, but reliability and accuracy may deteriorate due to false positives
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors its own performance and adjusts its analysis parameters. The classification neural network provides feedback to the delineation network, allowing the system to learn from previous classifications and improve its accuracy over time, reducing false positives while maintaining high productivity.
Solution Approach 2:
The patent replaces traditional rule-based mechanical analysis systems with neural network-based automated analysis. This substitution enables the system to handle complex patterns and contextual information across multiple beats more effectively, improving both speed and reliability by learning from training data rather than relying on fixed thresholds and rules.
3Measurement precision
If contextual information across multiple beats is analyzed, then measurement precision in identifying abnormalities is improved, but loss of time for processing increases
Solution Approach 1:
The patent performs preliminary grouping of beats using the grouping neural network before detailed classification. This preliminary action organizes the data in a way that facilitates faster subsequent analysis, allowing the system to efficiently utilize contextual information across multiple beats without excessive processing time delays.
Solution Approach 2:
The patent employs dynamic processing where the system adapts its analysis depth and scope based on the characteristics of the ECG signal. For routine signals, faster processing is used, while signals showing potential abnormalities trigger more comprehensive multi-beat analysis, optimizing the balance between accuracy and processing time.
Data Source
AI summary
Systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using a substantial amount of ECG data. The systems receive ECG data from a sensing device positioned on a patient such as one or more ECG leads. The system may include an application that communicates with an ECG platform running on a server(s) that processes and analyzes the ECG data, e.g., using neural networks for delineation of the cardiac signal and classification of various abnormalities, conditions and/or descriptors. The ECG platform may further process and analyze the ECG data using neural networks and/or algorithms for embedding and grouping. The processed ECG data is used to generate a graphic user interface that is communicated from the server(s) to a computer for display in a user-friendly and interactive manner with enhanced accuracy.