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

被引:0
作者
Hammour, Ghena [1 ,3 ]
Davies, Harry [1 ,3 ]
Atzori, Giuseppe [2 ,3 ]
Della Monica, Ciro [2 ,3 ]
Ravindran, Kiran K. G. [2 ,3 ]
Revell, Victoria [2 ]
Dijk, Derk-Jan [2 ,3 ]
Mandic, Danilo P. [1 ,3 ]
机构
[1] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2BT, England
[2] Univ Surrey, Fac Hlth & Med Sci, Surrey Sleep Res Ctr, Sch Biosci, Guildford GU2 7XH, England
[3] UK Dementia Res Inst, Care Res & Technol Ctr, London SW7 2BT, England
关键词
Sleep; Electroencephalography; Brain modeling; Data models; Scalp; Recording; Sensors; Automatic sleep scoring; hearables; ear-EEG; machine learning; wearable EEG; AMERICAN ACADEMY;
D O I
10.1109/JTEHM.2024.3388852
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页码:448 / 456
页数:9
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