M4CEA: A Knowledge-guided Foundation Model for Childhood Epilepsy Analysis.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yuanmeng Feng, Dinghan Hu, Tiejia Jiang, Feng Gao, Jiuwen Cao
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引用次数: 0

Abstract

Existing electroencephalogram (EEG)-based deep learning models are mainly designed for single or several specific tasks in childhood epilepsy analysis, which limits the perceptual capabilities and generalisability of the model. Recently, Foundation Models (FMs) achieved significant success in medical analysis, motivating us to explore the capability of FMs in childhood epilepsy analysis. The objective is to construct a FM with strong generalization capability on multi-tasking childhood epilepsy analysis. To this end, we propose a knowledge-guided foundation model for childhood epilepsy analysis (M4CEA) in this paper. The main contributions of the M4CEA are using the knowledge-guided mask strategy and the temporal embedding of the temporal encoder, which allow the model to effectively capture multi-domain representations of childhood EEG signals. Through pre-training on an EEG dataset with more than 1,000 hours childhood EEG recording, and performance fine-tuning, the developed M4CEA model can achieve promising performance on 8 downstream tasks in childhood epilepsy analysis, including artifact detection, onset detection, seizure type classification, childhood epilepsy syndrome classification, hypoxic-ischaemic encephalopathy (HIE) grading, sleep stage classification, epileptiform activity detection and spike-wave index (SWI) quantification. Taking HUH (Helsinki University Hospital) seizure detection task as an example, our model shows 9.42% improvement over LaBraM (a state-of-the-art Large Brain foundation Model for EEG analysis) in Balanced Accuracy. The source code and pre-trained weight are available at: https://github.com/Evigouse/M4CEA Project.

M4CEA:儿童癫痫分析的知识导向基础模型。
现有的基于脑电图(EEG)的深度学习模型主要针对儿童癫痫分析中的单个或多个特定任务而设计,这限制了模型的感知能力和通用性。近年来,基础模型(FMs)在医学分析中取得了显著的成功,这促使我们探索FMs在儿童癫痫分析中的能力。目的是构建具有较强泛化能力的多任务儿童癫痫分析模型。为此,我们提出了一个知识导向的儿童癫痫分析基础模型(M4CEA)。M4CEA的主要贡献是使用知识引导的掩模策略和时间编码器的时间嵌入,这使得该模型能够有效地捕获儿童脑电信号的多域表示。通过对1000小时以上儿童脑电图数据集的预训练和性能微调,所开发的M4CEA模型在儿童癫痫分析的8个下游任务上取得了令人满意的性能,包括伪像检测、发作检测、癫痫发作类型分类、儿童癫痫综合征分类、缺氧缺血性脑病(HIE)分级、睡眠阶段分类、癫痫样活动检测及峰波指数(SWI)量化。以HUH(赫尔辛基大学医院)癫痫检测任务为例,我们的模型在平衡精度上比LaBraM(最先进的脑电图分析大脑基础模型)提高了9.42%。源代码和预训练的权重可在:https://github.com/Evigouse/M4CEA项目。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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