ECG Feature Pre-Training With Unlabeled Signals for Accurate Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of constructing high-performance classification models for electrocardiogram (ECG) data is exacerbated by the scarcity of labeled data, which is costly to obtain and often includes unlabeled and computationally erroneous data, making it difficult to achieve accurate labeling and model performance.
Innovation Solution
A self-supervised learning method is employed to pre-train an ECG feature extraction model by extracting features from unlabeled data using a rule-based feature extractor and an encoder, followed by transfer learning with labeled data to minimize loss functions, enabling the model to output accurate ECG labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large amount of labeled ECG data is collected to construct a high-performance classification model, then the model accuracy is improved, but the cost and time required for manual labeling by medical staff increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the encoder model on unlabeled ECG data before the actual classification task. This pre-training phase extracts useful features and patterns from the data in advance, so that when labeled data becomes available, the model is already optimized and requires less manual labeling to achieve high accuracy, thereby reducing the time and cost of the labeling process
Solution Approach 2:
The patent implements self-service through self-supervised learning where the model automatically learns from unlabeled ECG data without requiring manual annotations. The system uses the data itself to create supervision signals, enabling the model to improve its performance autonomously without continuous human intervention for labeling
2Reliability
If manual labeling by medical staff is performed to create labeled ECG data, then the quality of training data is improved, but the cost increases significantly
Solution Approach 1:
The patent implements self-service through self-supervised learning where the model automatically learns from unlabeled ECG data without requiring manual annotations. The system uses the data itself to create supervision signals, enabling the model to improve its performance autonomously without continuous human intervention for labeling, thereby reducing costs while maintaining data quality
Solution Approach 2:
The patent applies preliminary action by pre-training the encoder model on unlabeled ECG data before the actual classification task. This pre-training phase extracts useful features and patterns from the data in advance, so that when labeled data becomes available, the model is already optimized and requires less manual labeling to achieve high accuracy, thereby reducing the time and cost of the labeling process
3Quantity of substance
If generation models are used to create synthetic ECG data to increase dataset size, then the quantity of training data is improved, but the labeling accuracy deteriorates because the models cannot recognize which photographs correspond to generated data
Solution Approach 1:
The patent implements self-service through self-supervised learning where the model automatically learns from unlabeled ECG data without requiring manual annotations. The system uses the data itself to create supervision signals, enabling the model to improve its performance autonomously without continuous human intervention for labeling, thereby reducing costs while maintaining data quality
Solution Approach 2:
The patent applies preliminary action by pre-training the encoder model on unlabeled ECG data before the actual classification task. This pre-training phase extracts useful features and patterns from the data in advance, so that when labeled data becomes available, the model is already optimized and requires less manual labeling to achieve high accuracy, thereby reducing the time and cost of the labeling process
Data Source
AI summary
A deep neural network pre-training method for classifying electrocardiogram (ECG) data and a device for the same are disclosed. A method for training an ECG feature extraction model may include receiving a ECG signal, extracting one or more first features related to the ECG signal by inputting the ECG signal to a rule-based feature extractor or a neural network model, extracting at least one second feature corresponding to the at least one first feature by inputting the ECG signal to an encoder, and pre-training the ECG feature extraction model by inputting the at least one second feature into at least one of a regression function and a classification function to calculate at least one output value. The pre-training of the ECG feature extraction model may include training the encoder to minimize a loss function that is determined based on the at least one output value and the at least one first feature.