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

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Ghena Hammour;Harry Davies;Giuseppe Atzori;Ciro Della Monica;Kiran K. G. Ravindran;Victoria Revell;Derk-Jan Dijk;Danilo P. Mandic
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引用次数: 0

Abstract

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.
从头皮到耳部电子脑电图:用于老年人自动睡眠评分的通用迁移学习模型
目的:睡眠监测广泛使用了从头皮收集的脑电图(EEG)数据,从而产生了非常庞大的数据存储库和训练有素的分析模型。然而,对于新兴的、侵入性较低的模式,如耳部脑电图,却缺乏这种丰富的数据:目前的研究试图通过直接或通过最小微调应用数据预训练模型来利用大量的开源头皮脑电图数据集;这是在使用单个耳内电极记录的耳部脑电图数据进行有效睡眠分析的背景下实现的,该数据以同侧乳突为参照,并在我们之前的工作中进行了内部开发。与之前的研究不同,我们的研究独特地将重点放在了老年人群(17 名受试者,年龄在 65-83 岁之间,平均年龄为 71.8 岁,其中一些人患有健康疾病)上,并采用 LightGBM 进行迁移学习,与之前的深度学习方法有所不同。结果结果显示,预训练模型在耳-EEG 上的初始准确率为 70.1%,但利用耳-EEG 数据对模型进行微调后,其分类准确率提高到 73.7%。微调后的模型对 13 位参与者中的 10 位有显著的统计学改进(P < 0.05,依赖性 t 检验),这体现在平均科恩卡帕分数(衡量分类项目中评分者之间一致性的统计学指标)提高到了 0.639,表明睡眠阶段的自动分类与专家分类之间的一致性更强了。SHAP值比较分析表明,N3睡眠阶段的特征重要性发生了变化,凸显了微调过程的有效性:我们的研究结果凸显了在耳部脑电图数据上微调预训练头皮脑电图模型以提高分类准确性的潜力,尤其是在老年人群中使用基于特征的迁移学习方法。这种方法为睡眠研究中的耳部脑电图分析提供了一个前景广阔的途径,为迁移学习在不同人群和计算技术中的适用性提供了新的见解:临床影响:增强型耳部电子脑电图方法在远程监测设置中可能会起到关键作用,可对患有痴呆症或睡眠呼吸暂停等疾病的老年患者进行连续、无创的睡眠质量评估。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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