Sequentially-Reduced Neural Network for Cardiovascular Waveform Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for extracting waveforms and cardiovascular indices from clinical measurements are computationally expensive and time-consuming, hindering real-time analysis, while general-purpose function approximators like machine learning offer speed and accuracy but require efficient classification models for reliable cardiovascular disease diagnosis.
Innovation Solution
A sequentially-reduced feedforward neural network model is used to non-invasively and instantaneously transfer radial and/or brachial waveforms to carotid waveforms or their reduced-order parameters, enabling rapid determination of cardiovascular indices and biomarkers, employing Fourier-based custom loss functions and various neural network architectures for efficient training and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inverse/minimization problems are solved using repeated calculation of forward Navier-Stokes models, then measurement precision and reliability are improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training a machine learning model using extensive training data and computational resources before actual use. The model learns the complex mappings between input waveforms and cardiovascular indices during training, so that during real-time application, it can quickly predict results without performing expensive iterative Navier-Stokes calculations. This shifts the heavy computational work from the execution phase to the training phase.
Solution Approach 2:
The patent replaces the mechanical computational system (iterative Navier-Stokes equations) with an AI-based system (machine learning model). Instead of solving complex fluid dynamics equations repeatedly, the system uses a trained neural network that has captured the essential relationships between waveforms and cardiovascular parameters, substituting physical computation with data-driven prediction.
2Productivity
If general-purpose machine learning function approximators are used, then computational speed and universality are improved, but model reliability and diagnostic accuracy for cardiovascular diseases deteriorate
Solution Approach 1:
The patent applies local quality by customizing the machine learning model specifically for cardiovascular waveform analysis, rather than using a generic model. The model architecture, training data, and loss functions are all tailored to the specific requirements of cardiovascular diagnostics, ensuring that the model captures the nuanced patterns and relationships specific to this domain while maintaining speed advantages.
Solution Approach 2:
The patent employs parameter changes by using Fourier-based custom loss functions that transform the output into frequency-domain representations. This allows the model to capture temporal patterns and physiological characteristics in the frequency domain, improving diagnostic accuracy while maintaining computational efficiency through the mathematical properties of Fourier transforms.
3Reliability
If complex neural network architectures are used to improve classification accuracy, then reliability of cardiovascular disease diagnosis is improved, but device complexity and training difficulty increase
Solution Approach 1:
The patent applies segmentation by dividing the complex waveform analysis task into manageable components: waveform preprocessing, feature extraction using Fourier transforms, and classification. This modular approach simplifies the overall system design while maintaining high accuracy, as each component can be optimized independently and the complexity is distributed across manageable stages rather than concentrated in a single complex model.
Data Source
AI summary
Systems, methods, devices, and machine readable media storing instructions (programming) for an instantaneous or nearly instantaneous (e.g., within 0.1 seconds, within 0.001 seconds, etc.), non-invasive, and easy-to-use transfer from a radial and/or brachial waveform to a carotid waveform or its reduced-order parameters are described. Some embodiments relate to systems, methods, devices, and programming for determining cardiovascular (clinical) indices and biomarkers from two or more of the radial and/or brachial and/or carotid waveforms (or their corresponding reduced-order representations).