Textual ECG Model Training with Temporal Patch Masking
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Solution Overview
Problem
Current self-supervised machine learning processes for electrocardiogram data require image transformation, leading to increased memory footprint and inefficiency due to high processing power needs, and necessitate conversion of signals into images, further reducing efficiency.
Innovation Solution
A system and method for training machine learning models using unlabeled electrocardiogram data by receiving ECG data in textual format, creating overlapping temporal patches, masking these patches, pretraining a model to predict masked patches, adjusting model parameters, and training with labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image transformation techniques are used for self-supervised machine learning of ECG data, then the model can process electrocardiogram signals, but the memory footprint increases and processing efficiency decreases
Solution Approach 1:
The patent extracts and removes the unnecessary image transformation step from the traditional machine learning pipeline. Instead of converting ECG signals to images and then processing them, the system directly processes the raw ECG signal data through temporal patching and masking operations, eliminating the intermediate image representation that consumes excessive memory resources.
Solution Approach 2:
The patent replaces the mechanical image transformation and processing system with a direct signal processing approach. Instead of using image-based neural networks that require converting ECG data to visual representations, the system employs text-based temporal patching and masking techniques that operate directly on the numerical ECG signal data, substituting an inefficient mechanical conversion process with a more direct computational approach.
2Ease of manufacture
If image transformation is applied to ECG signals for machine learning, then signal processing can be performed, but processing power requirements increase significantly
Solution Approach 1:
The patent extracts and eliminates the computationally intensive image transformation step from the processing pipeline. By removing the conversion of ECG signals to images and subsequent image processing operations, the system significantly reduces the processing power required while maintaining the core capability of analyzing ECG data through direct temporal pattern recognition.
Solution Approach 2:
The patent substitutes the power-intensive image processing mechanical system with a more efficient direct signal processing approach. Instead of using GPU-heavy image transformation and convolutional neural network operations, the system employs CPU-efficient temporal patching and masking operations that work directly on the numerical ECG data structure, reducing overall processing power requirements.
3Extent of automation
If ECG signals are converted into images for self-supervised learning, then the learning process can proceed, but the overall process efficiency decreases
Solution Approach 1:
The patent extracts and removes the inefficient image conversion step from the self-supervised learning process. By eliminating the transformation of ECG signals to images, the system maintains full self-supervised learning capability while significantly improving process efficiency, as the model can now directly learn temporal patterns from the raw signal data without unnecessary conversion overhead.
4Ease of manufacture
If image transformation and intensive processing techniques are used, then machine learning models can be trained on ECG data, but computational efficiency decreases
Solution Approach 1:
The patent substitutes the inefficient image-based mechanical processing system with a direct text-based signal processing approach. Instead of converting ECG data to images and using image processing techniques, the system employs temporal patching and masking operations that work directly on the numerical signal data, maintaining model training capability while dramatically improving computational efficiency and reducing resource consumption.
Data Source
AI summary
A system for training machine learning models with unlabeled electrocardiogram signals, the system including a memory containing instructions configurating a processor to receive a plurality of electrocardiogram (ECG) data in a textual format, create one or more overlapping temporal patches from the plurality of ECG data, mask at least one temporal patch from the one or more overlapping temporal patches, pretrain an ECG machine learning model to predict the at least one masked temporal patch from the one or more overlapping temporal patches, adjust one or more parameter values of the ECG machine learning model as a function of the at least one predicted masked temporal patch and the at least one masked temporal patch and train the ECG machine learning model as a function of the one or more parameter values and a labeled set of ECG training data.