Neural Network Bioelectric Signal Compensation for Stroke Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing imaging-based technologies for stroke detection, such as MRI and CT scans, are resource-intensive, costly, and not readily accessible in remote areas, while direct deployment of deep learning models across different devices results in significant accuracy loss due to hardware inconsistencies, leading to inefficient stroke diagnosis.
Innovation Solution
A system that generates a compensation factor based on simulated bioelectric signals from both devices to align and adjust patient bioelectric signals, allowing for accurate training and deployment of a classification neural network across different microwave imaging devices, ensuring consistent data quality and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a classification neural network is trained on data from a first computing device and deployed on a second computing device, then the model can be utilized across multiple devices, but significant accuracy loss occurs due to hardware inconsistencies
Solution Approach 1:
The patent applies parameter changes by generating a compensation factor that adjusts the statistical parameters (mean and standard deviation) of the training data to match the distribution characteristics of the target device. This transformation modifies the data parameters rather than the model structure, enabling the model to adapt to different hardware while maintaining classification accuracy.
Solution Approach 2:
The compensation factor acts as an intermediary between the source device data and the target device requirements. It mediates the transformation by serving as a bridge that translates data from one device's distribution characteristics to another, eliminating the need for direct retraining while preserving accuracy.
2Measurement precision
If extensive data collection and retraining is performed for each new device, then measurement precision is maintained, but resource consumption and training time increase significantly
Solution Approach 1:
The patent implements preliminary action by pre-computing the compensation factor using simulated data that captures device-specific characteristics. This preparation is done in advance before actual deployment, so when the model needs to be transferred to a new device, the adaptation is immediate and requires no time-consuming retraining, thus maintaining both accuracy and efficiency.
Solution Approach 2:
Instead of collecting extensive real data from each new device, the patent creates a compensated copy of the original training data by applying the compensation factor. This synthetic adapted data replicates the statistical properties of the target device without requiring physical data collection, significantly reducing resource consumption while maintaining model performance.
3Reliability
If clinical assessment methods are used for stroke detection, then medical diagnosis can be performed, but the process is subjective and time-consuming
Solution Approach 1:
The patent replaces the mechanical system of manual clinical assessment with an automated neural network-based classification system. The neural network processes bioelectric signals automatically, substituting human expert analysis with algorithmic evaluation, which eliminates subjectivity and dramatically reduces detection time while maintaining diagnostic reliability.
4Measurement precision
If imaging techniques such as MRI and CT scans are used for stroke detection, then detection accuracy is improved, but the methods are expensive and resource-intensive
Solution Approach 1:
The patent employs a lightweight neural network model that can be deployed on resource-constrained devices, replacing expensive and complex imaging equipment like MRI and CT scanners. The model processes readily available bioelectric signals to achieve stroke detection accuracy comparable to advanced imaging, making the solution accessible in remote and resource-limited settings.
Data Source
AI summary
Embodiments of a system for training a classification neural network are provided. The system is configured to receive a first set of simulated bioelectric signals and patient bioelectric signals from a first computing device and a second set of simulated bioelectric signals from a second computing device, generate a compensation factor for the second computing device based on the first set of simulated bioelectric signals and the second set of simulated bioelectric signals, generate compensated patient bioelectric signals based on the compensation factor and the patient bioelectric signals, and train the classification neural network based on the compensated patient bioelectric signals, the second set of simulated bioelectric signals and the compensation factor. The classification neural network is trained to predict a classification label for each of one or more bioelectric signals.