Sleep Event Classification Model Finetuning for Data Scarcity
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Solution Overview
Problem
Existing methods for training sleep-related event classification models are not generally applicable and fail to effectively address data scarcity and mismatches between source and target populations.
Innovation Solution
A target population-based finetuning method for sleep-related event classification models, which involves providing a pre-trained model and finetuning it using second training data associated with a target population, thereby improving classification performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a model is pre-trained on large publicly available datasets to overcome data scarcity, then the model can be applied to smaller datasets of interest, but mismatches occur between datasets due to changes in population characteristics and sensor location
Solution Approach 1:
The model is pre-trained on large publicly available datasets before being applied to smaller target datasets. This preliminary training action allows the model to learn general sleep event patterns from abundant data, and then these learned patterns are transferred to the specific target population, overcoming the limitation of scarce target data while maintaining classification accuracy through subsequent adaptation
Solution Approach 2:
The training process involves changing parameters by transitioning from training on source population data to fine-tuning on target population data. This parameter change approach allows the model to adapt its learned representations to match the specific characteristics of the target population, resolving the mismatch issue while preserving the benefits of large-scale pre-training
2Adaptability or versatility
If transfer learning is used to tune a sleep staging model to a new electrode location or specific subject, then the model adapts to the target domain, but the approach is only applicable to very specific use cases and not generally applicable
Solution Approach 1:
The system achieves universality by implementing a general fine-tuning framework that can be applied across different target populations and use cases. Rather than creating specialized solutions for each specific scenario (e.g., electrode location changes or subject-specific tuning), the same fine-tuning approach can be universally applied to any target population, making the method multi-functional and broadly applicable while maintaining adaptability
Data Source
AI summary
Disclosed is a method of training a sleep-related event classification model. The method may comprise a step of providing a pre-trained classification model. The classification model may be configured to classify one or more sleep-related events of a subject based on one or more physiological measurements of the subject. The classification model may be pre-trained using first training data associated with a source population of subjects. The method may comprise a step of finetuning the classification model using second training data associated with a target population of subjects. In addition, a corresponding method of classifying one or more sleep-related events of a subject as well as a corresponding computer program, data processing apparatus or system and data structure is provided.