From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

Autor:	Hammour G; Department of Electrical and Electronic EngineeringImperial College London SW7 2BT London U.K.; U.K. Dementia Research Institute, Care Research and Technology Centre SW7 2BT London U.K., Davies H; Department of Electrical and Electronic EngineeringImperial College London SW7 2BT London U.K.; U.K. Dementia Research Institute, Care Research and Technology Centre SW7 2BT London U.K., Atzori G; 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of Surrey GU2 7XH Guildford U.K.; U.K. Dementia Research Institute, Care Research and Technology Centre SW7 2BT London U.K., Della Monica C; 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of Surrey GU2 7XH Guildford U.K.; U.K. Dementia Research Institute, Care Research and Technology Centre SW7 2BT London U.K., Ravindran KKG; 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of Surrey GU2 7XH Guildford U.K.; U.K. Dementia Research Institute, Care Research and Technology Centre SW7 2BT London U.K., Revell V; 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of Surrey GU2 7XH Guildford U.K., Dijk DJ; 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of Surrey GU2 7XH Guildford U.K.; U.K. Dementia Research Institute, Care Research and Technology Centre SW7 2BT London U.K., Mandic DP; Department of Electrical and Electronic EngineeringImperial College London SW7 2BT London U.K.; U.K. Dementia Research Institute, Care Research and Technology Centre SW7 2BT London U.K.
Jazyk:	angličtina
Zdroj:	IEEE journal of translational engineering in health and medicine [IEEE J Transl Eng Health Med] 2024 Apr 17; Vol. 12, pp. 448-456. Date of Electronic Publication: 2024 Apr 17 (Print Publication: 2024).
DOI:	10.1109/JTEHM.2024.3388852
Abstrakt:	Objective: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. Methods and Procedures: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. Results: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. Conclusion: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. Clinical Impact: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea. (© 2024 The Authors.)
Databáze:	MEDLINE
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