ECG Processing System Neural Network Analysis
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Solution Overview
Problem
Current ECG analysis systems face challenges in accurately and efficiently detecting cardiac events and abnormalities, particularly due to reliance on handcrafted features, suboptimal wavelet-based and Hidden Markov Model approaches, and limitations in capturing contextual information, leading to false positives and incomplete analysis.
Innovation Solution
A system utilizing machine learning algorithms and medical-grade artificial intelligence, including neural networks for delineation and classification, to analyze ECG data from multiple leads, enabling accurate detection and prediction of cardiac arrhythmias and abnormalities, such as atrial fibrillation, by processing data in the cloud and providing user-friendly interactive reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If handcrafted features and traditional algorithms (wavelet-based, HMM) are used for ECG analysis, then the system is simpler to implement, but the accuracy and reliability of cardiac event detection deteriorates due to false positives and incomplete analysis
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods (wavelet transforms, Hidden Markov Models, handcrafted feature extraction) with a deep learning-based neural network system. The neural network automatically learns optimal features from raw ECG signals, eliminating the need for manual feature engineering and traditional algorithmic approaches, thereby improving detection accuracy while maintaining implementation feasibility through standardized deep learning frameworks
Solution Approach 2:
The patent transforms the analysis approach by changing from fixed-parameter traditional algorithms to adaptive-parameter deep learning models. The neural network dynamically adjusts feature extraction parameters and classification thresholds based on learned patterns from training data, enabling the system to adapt to varying ECG signal characteristics and improve reliability across different patient populations
2Speed
If traditional ECG analysis methods are used, then the processing speed is faster with simpler algorithms, but the productivity and comprehensiveness of cardiac event detection deteriorates due to inability to capture contextual information
Solution Approach 1:
The patent enhances the analysis by adding temporal context as an additional dimension. The neural network processes not only individual ECG beats but also sequences of beats and broader temporal patterns, capturing contextual information across time. This multi-scale temporal analysis enables the system to detect arrhythmias and predict cardiac events with greater comprehensiveness while maintaining efficient processing through optimized deep learning architectures
3Measurement precision
If telecardiology centers with trained professionals are used, then the diagnostic quality is high, but the accessibility and cost-effectiveness deteriorates due to slow and expensive processes
Solution Approach 1:
The patent implements an automated ECG analysis system that performs diagnostic functions independently without requiring trained cardiologists to manually review each ECG. The deep learning model automatically detects abnormalities, classifies arrhythmias, and generates diagnostic reports, enabling the system to serve itself and provide high-quality diagnostics at scale. This automation maintains diagnostic accuracy while dramatically improving accessibility and reducing costs
Solution Approach 2:
The patent creates a digital copy of expert cardiologist diagnostic capabilities through the trained neural network. The model learns from extensive training data to replicate the pattern recognition and decision-making abilities of trained professionals. This digital copy enables the system to provide expert-level diagnostic quality across multiple locations simultaneously, improving accessibility without sacrificing diagnostic precision
4Ease of manufacture
If current software interpretation systems are used, then the cost is reduced compared to telecardiology centers, but the reliability deteriorates due to low quality interpretation and false positives
Solution Approach 1:
The patent replaces rule-based software interpretation systems with a deep learning-based neural network. Instead of relying on predefined rules and handcrafted features that limit accuracy, the neural network learns complex patterns directly from training data, significantly improving interpretation accuracy and reducing false positives while maintaining cost-effectiveness through automated processing
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/electrodes that may be integrated in a smart device. 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, to detect and/or predict various abnormalities, conditions and/or descriptors. The system may also determine a confidence score corresponding to the abnormalities, conditions and/or descriptors. 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.