Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning (1907.13177v3)

Abstract: Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. Methods: We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. The target domains are purposely adopted to cover different degrees of data mismatch to the source domains. Results: Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach. Conclusions: These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues. Significance: As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small. The source code and the pretrained models are available at http://github.com/pquochuy/sleep_transfer_learning.

• The paper introduces a deep transfer learning framework that adapts pre-trained sleep staging models to diverse datasets.
• It employs SeqSleepNet+ and DeepSleepNet+ architectures, fine-tuning models initially trained on the MASS database to achieve up to 10.9% accuracy improvement.
• The methodology mitigates data scarcity and modality mismatch, offering a scalable solution for enhancing biomedical signal processing in sleep research.

Deep Transfer Learning for Enhanced Automatic Sleep Staging

The paper "Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning" details a sophisticated approach to addressing challenges in automatic sleep staging by employing deep transfer learning. This research is a pertinent contribution to the domain of biomedical signal processing, particularly in sleep research, wherein the accurate identification of sleep stages is pivotal yet often constrained by limited cohort sizes and data variability.

The authors have introduced a transfer learning methodology that effectively leverages a large dataset to improve performance when applied to smaller cohorts. The paper primarily utilized the Montreal Archive of Sleep Studies (MASS) database, featuring recordings from 200 subjects, as the source domain. The target domains encompassed three different datasets: Sleep-EDF-SC, Sleep-EDF-ST, and Surrey-cEEGrid, each characterized by diverse degrees of data mismatch in relation to the source domain.

Methodology

The underlying approach is characterized by adapting pretrained networks initially developed on a large dataset to smaller, diverse target datasets through a process of fine-tuning. The authors utilized two deep learning architectures, SeqSleepNet+ and DeepSleepNet+, derived from a generic sequence-to-sequence sleep staging framework. This framework allows for processing single or multi-channel inputs and integrates epoch-level and sequence-level feature learning through dedicated processing blocks (EPBs and SPBs).

SeqSleepNet+ employs a time-frequency representation of EEG data and uses an attentional bidirectional RNN for sequence processing. In contrast, DeepSleepNet+ processes raw signals directly via a dual-branch CNN structure and stacked bidirectional LSTMs for sequence analysis. Both architectures were pretrained on the MASS data and subsequently fine-tuned across the target databases.

Key Findings and Results

The research highlights significant improvements in sleep staging accuracy across all target domains when employing transfer learning. Notably, using finetuning techniques, SeqSleepNet+ achieved accuracy improvements of 2.5% on Sleep-EDF-SC, 2.0% on Sleep-EDF-ST, and 1.4% on Surrey-cEEGrid over scratch models. DeepSleepNet+ demonstrated even more substantial improvements, with accuracy gains of 3.4%, 7.1%, and 10.9% on the respective datasets.

A profound advantage of the proposed method is its ability to combat the detrimental effects of severe data mismatch, typical in cross-modality scenarios, where EEG-pretrained models were effectively adapted to analyze EOG data, resulting in significant performance enhancements.

Implications and Future Directions

The approach delineated in this paper offers a scalable solution to the perennial issue of limited data in automatic sleep staging and potentially other biomedical domains. The transfer learning methodology not only improves the efficacy of sleep staging algorithms on small and heterogeneous datasets but also significantly reduces the need for exhaustive manual labeling and long training times associated with developing models from scratch.

Future research could explore further optimization of finetuning strategies and the application of transfer learning to a broader range of biomedical signals and conditions. Moreover, the potential for integrating continuous learning paradigms and semi-supervised approaches alongside transfer learning to maximize data utility in sparse labeling environments may represent a promising direction.

In summary, the paper presents a robust framework for enhancing sleep staging models' generalization capabilities, affirming the transformative potential of deep learning methodologies in overcoming limitations imposed by small and varied datasets within the realms of sleep research and beyond.