摘要
早期监测早产儿脑功能发育及损伤有非常重要的临床价值。目前评价其脑发育及脑损伤的检测工具颇多，主要为各类影像学及行为神经学检查。多年来人们一直在探索各种检查方法的综合应用，其中不需执行特殊任务、无创、无不良反应的静息态功能磁共振和脑电图的联合应用在小儿脑功能研究中有着突出的优势。应用人工智能的支持向量机技术，可整合2个模态数据的典型特征，从而获得一个多模态的早产儿脑功能发育评估和脑损伤诊断系统。
It is critical to assess the development and injury of brain function in premature infants in early stage. Currently, brain development and brain injury are evaluated by many ways and mainly by kinds of imaging and behavioral neurological assessment. The integrated application of these examination methods have been explored for years. The combination of resting - state functional magnetic resonance imaging and electroencephalography has promi- nent advantages, because they do not need to perform specific tasks, and they are noninvasive and non - toxic for pre- term infants. Based on the support vetor machine learning of artificial intelligence technology, the typical characteristics of 2 muhimodal data can be integrated and a muhimodal assessment system which automatically monitor and predict premature infants＇ brain function development and injury can be established.
出处
《中华实用儿科临床杂志》
CSCD
北大核心
2017年第14期1055-1057,共3页
Chinese Journal of Applied Clinical Pediatrics
基金
国家自然科学基金（81173662）
关键词
婴儿
早产
脑功能
多模态
静息态功能磁共振
脑电图
Infant, premature
Brain function
Multi - mode
Resting - state functional magnetic resonanceimaging
Electroeneephalography
