Sleep Stage Classification With Self-Supervised Transfer Learning
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Solution Overview
Problem
Sleep stage classification methods using supervised learning models face limitations due to the need for extensive labeling and label quality dependence, leading to performance constraints on specific datasets.
Innovation Solution
A self-supervised learning-based approach using an unsupervised learning-based adversarial generative model for sleep stage classification, employing transfer learning and attention-based signal augmentation to generate positive pairs and minimize cosine similarity, enabling accurate classification with a small number of labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning models are used for sleep stage classification, then classification accuracy can be improved through extensive labeling and training, but the method becomes dependent on label quality and dataset-specific performance
Solution Approach 1:
The patent applies self-supervised learning to pre-train the model on large amounts of unlabeled sleep data before fine-tuning with limited labeled data. This preliminary action of learning from unlabeled data enables the model to capture general sleep patterns and features, improving its ability to generalize to different datasets while reducing dependency on extensive labeling.
2Reliability
If extensive labeling is performed to improve supervised learning model performance, then training can be enhanced, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent employs self-supervised learning where the model automatically generates its own training signals from unlabeled data without requiring manual annotation. This self-service approach allows the model to learn from the structure and patterns in the raw sleep data itself, eliminating the need for time-consuming manual labeling while still achieving high performance through self-generated supervision signals.
3Adaptability or versatility
If self-supervised learning is used to classify sleep stages with small number of labels, then dataset generalization is improved, but the model requires sophisticated training techniques
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) self-supervised pre-training on large unlabeled datasets to learn general representations, and (2) supervised fine-tuning on small labeled datasets for specific classification tasks. This segmentation allows the model to first capture universal sleep patterns and then adapt to specific datasets with minimal labels, reducing the complexity burden on any single training stage.
Data Source
AI summary
Disclosed are a method and system for training a self-supervised learning based-sleep stage classification model using a small number of labels. The sleep stage classification method performed by a computer system according to an embodiment may comprising the steps of: receiving polysomnography data to be inputted into a self-supervised learning based-sleep stage classification model; and classifying sleep stages from the polysomnography data by using the self-supervised learning based-sleep stage classification model, wherein the self-supervised learning based-sleep stage classification model is trained on patterns for sleep stage classification from new sleep data through transfer learning by fine-tuning weights on the basis of a representation learning model that is trained on representations from sleep signal data.