ECG Image Augmentation From Signal Data for Model Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Building prediction models based on electrocardiogram (ECG) images is challenging due to varying image quality issues such as poor capture quality, lighting, angle, focus, and motion artifacts.
Innovation Solution
An apparatus and method for training a machine learning model that includes a processor and memory to receive signal data, generate a digital image, apply image processing to produce an augmented image, and train the model using the augmented image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image processing is applied to improve ECG image quality, then image quality improves, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary actions by generating synthetic ECG images with known ground truth labels before actual model training. This pre-processing step creates a standardized training dataset that eliminates the need for complex post-processing of varied quality images, thereby improving image quality consistency without proportionally increasing processing complexity.
Solution Approach 2:
The invention creates copies of ECG images through synthetic generation from signal data. Instead of processing and enhancing numerous low-quality captured images, the system generates clean synthetic copies with guaranteed quality and accurate labels, significantly reducing the complexity of image quality improvement while maintaining high manufacturing precision.
2Reliability
If more varied ECG images are used for training, then model accuracy improves, but data quality consistency deteriorates
Solution Approach 1:
The system generates multiple copies of ECG images synthetically from signal data, ensuring each copy has consistent quality and accurate ground truth labels. This approach provides varied training data for improved model accuracy while maintaining uniform data quality composition, resolving the contradiction between reliability and stability.
Solution Approach 2:
The invention changes parameters by generating images with controlled variations in synthetic conditions while maintaining consistent quality standards. This allows the training data to be varied enough for accurate model learning while preserving composition stability through standardized generation parameters and quality control.
3Measurement precision
If manual labeling of ECG images is performed, then training data accuracy improves, but time and labor requirements increase
Solution Approach 1:
The system creates synthetic image copies with automatically generated ground truth labels derived from the original signal data. This eliminates manual labeling entirely while maintaining high label accuracy, as the labels are mathematically derived from the source signal rather than subjectively assigned by annotators.
Solution Approach 2:
The invention replaces the mechanical process of manual labeling with an automated computational system. The ground truth labels are generated algorithmically from signal data through image processing, substituting human labor with machine-based automatic label generation that is both faster and more consistent.
Data Source
AI summary
An apparatus and method for training a machine learning model to augment signal data and image data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a signal data. The memory instructs the processor to generate a digital image, wherein the digital image comprises the signal data. The memory instructs the processor to transmit the digital image to an image processing module, wherein the image processing module produces an augmented image. The memory instructs the processor to transmit the signal data to a signal processing module, wherein the signal processing module produces the augmented image. The memory instructs the processor to train a machine learning model using the augmented image.