Biosignal Analysis Framework Using Segmented Waveform and DSP Models
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Solution Overview
Problem
Conventional ECG analysis methods, including digital signal processing algorithms and deep neural networks, are insufficient for comprehensive diagnosis of heart arrhythmias, as they lack the ability to provide clear explanations for their decisions and struggle with balancing model complexity and performance.
Innovation Solution
A framework that combines waveform and digital signal processing feature extraction modules with attention mechanisms, allowing for the selection of appropriate models to analyze ECG signals, providing both waveform and DSP features for comprehensive analysis and enabling understanding of diagnosis decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital signal processing algorithms are used for ECG analysis, then computational efficiency is maintained, but diagnostic accuracy and comprehensiveness are insufficient
Solution Approach 1:
The patent segments the ECG analysis task into multiple specialized models, each designed to extract specific types of features (waveform features, DSP features, attention features). This segmentation allows each model to focus on particular aspects of the signal, improving overall diagnostic accuracy while keeping individual model complexities manageable.
Solution Approach 2:
The patent merges multiple feature extraction approaches (waveform-based, DSP-based, and attention mechanisms) into a unified analysis framework. By combining these different methodologies, the system achieves comprehensive multi-perspective analysis that improves diagnostic accuracy beyond what any single approach could achieve alone.
2Measurement precision
If deep neural networks are used to improve multi-class classification accuracy, then diagnostic precision improves, but model complexity and computational requirements increase
Solution Approach 1:
The patent implements dynamic model selection based on the specific analysis task requirements. Different analysis scenarios can dynamically select appropriate combinations of waveform models, DSP models, and attention mechanisms, allowing the system to adapt model complexity to the specific diagnostic needs rather than always using the most complex configuration.
Solution Approach 2:
Different parts of the analysis pipeline use different levels of complexity appropriate to their specific function. Waveform models handle temporal pattern recognition, DSP models handle frequency and spectral analysis, and attention mechanisms handle feature weighting. Each component is optimized for its specific purpose rather than using a single monolithic complex model for all tasks.
3Measurement precision
If comprehensive feature extraction is performed to improve diagnostic accuracy, then measurement precision improves, but computational efficiency decreases
Solution Approach 1:
The patent performs preliminary feature extraction by separating waveform features and DSP features in advance, before the final classification stage. This preliminary organization of features allows the attention mechanism to efficiently weight and combine them without requiring redundant computation, improving overall computational efficiency while maintaining comprehensive feature analysis.
Solution Approach 2:
The attention mechanism serves as an intermediary that efficiently integrates features from multiple sources (waveform models and DSP models). Rather than having these models directly interact in a computationally intensive manner, the attention mechanism mediates by selectively weighting and combining their outputs, maintaining computational efficiency while achieving comprehensive analysis.
4Measurement precision
If multiple types of features are extracted from ECG signals, then diagnostic comprehensiveness improves, but the ability to provide clear explanations decreases
Solution Approach 1:
The attention mechanism provides feedback by generating attention weights that indicate the relative importance of different features and time points in the diagnostic decision. This feedback loop allows the system to not only make comprehensive multi-perspective diagnoses but also to explain which features were most influential in reaching each conclusion, maintaining interpretability despite comprehensive analysis.
Data Source
AI summary
A method and apparatus for performing a biosignal analysis task using a set of models includes receiving an input biosignal. Information that identifies the biosignal analysis task to be performed in association with the input biosignal is received. A waveform model and a digital signal processing (DSP) model are selected. A first type of feature and a second type of feature of the input biosignal are identified. An analysis model is selected, and the biosignal analysis task is performed using the analysis model.