目的通过对儿童复发性眩晕(recurrent vertigo of children,RVC)患儿进行视频脑电图(video-electroencephalogram,v-EEG)监测分析,探讨RVC患儿的v-EEG与疾病之间的关系。方法按照RVC诊断标准选取RVC患儿50例和正常对照组儿童20例,进一...目的通过对儿童复发性眩晕(recurrent vertigo of children,RVC)患儿进行视频脑电图(video-electroencephalogram,v-EEG)监测分析,探讨RVC患儿的v-EEG与疾病之间的关系。方法按照RVC诊断标准选取RVC患儿50例和正常对照组儿童20例,进一步根据脉搏氧监测结果将RVC组分为缺氧组和非缺氧组。所有入组儿童均行v-EEG监测,应用SPSS 25.0统计学软件对v-EEG结果进行统计学分析。结果RVC组与对照组的v-EEG异常率比较差异无统计学意义(P=0.871)。缺氧组、非缺氧组和对照组的组间v-EEG异常率比较差异无统计学意义(P=0.886)。结论v-EEG异常情况不能作为RVC患儿的诊断依据,但对RVC患儿的鉴别具有一定的临床价值。展开更多
为探索大脑与视觉之间的联系,提高大脑活动重建视频的清晰度与准确性,提出了一种名为高质量脑电视频重建(high quality electroencephalogram video reconstruction,HQEEGVR)的方法进行脑电信号重建视频。首先,提出三分支脑电特征提取...为探索大脑与视觉之间的联系,提高大脑活动重建视频的清晰度与准确性,提出了一种名为高质量脑电视频重建(high quality electroencephalogram video reconstruction,HQEEGVR)的方法进行脑电信号重建视频。首先,提出三分支脑电特征提取网络——掩蔽时空频融合网络(masking spatio-temporal frequency fusion network,MSTFFNet)从脑电信号中提取大脑活动信息,深入挖掘大脑活动变化背后的语义,同时提取时空频信息;其次,引入跨模态对比学习,对齐脑电、文本、图像特征,以便生成阶段使用;然后,提出级联视频扩散模型,具体来说,先利用稳定扩散模型以脑电特征为条件生成参考视频帧,接着以视频帧为参考,融入运动矢量,引入视频扩散模型捕捉视频时间特征;最终生成高质量视频。结果表明,该模型在重建视频的主体、动作、颜色、语义等方面表现较好。可见利用脑电信号可以捕获大脑活动的视觉与语义信息,从而重建高保真度和视觉真实性的视频。展开更多
The neonatal burst suppression is a severe EEG pattern and always demonstrates serious damage of nerve system. But the outcome of these patients depends on the different etiology. A total of 256 cases of video EEG rec...The neonatal burst suppression is a severe EEG pattern and always demonstrates serious damage of nerve system. But the outcome of these patients depends on the different etiology. A total of 256 cases of video EEG recordings were analyzed in order to summarize the etiology and outcome of burst suppression. The results showed that some patients in all 17 cases of burst suppression showed EEG improvement. The etiology was the dominant factor in long term outcome. It was suggested that effective video EEG monitoring is helpful for etiologic study and prognosis evaluation.展开更多
By the means of computing approximate entropy (ApEn) of video-EEG from some clinical epileptic, ApEn of EEG with epileptiform discharges is found significantly different from that of EEG without epileptiform discharge...By the means of computing approximate entropy (ApEn) of video-EEG from some clinical epileptic, ApEn of EEG with epileptiform discharges is found significantly different from that of EEG without epileptiform discharges, (p=0.002). Meanwhile, dynamic ApEn shows consistent change of EEG signal with discharges of epileptic waves inside. These results suggest that ApEn may be a useful tool for automatic recognition and detection of epileptic activity and for understanding epileptogenic mechanism.展开更多
文摘目的通过对儿童复发性眩晕(recurrent vertigo of children,RVC)患儿进行视频脑电图(video-electroencephalogram,v-EEG)监测分析,探讨RVC患儿的v-EEG与疾病之间的关系。方法按照RVC诊断标准选取RVC患儿50例和正常对照组儿童20例,进一步根据脉搏氧监测结果将RVC组分为缺氧组和非缺氧组。所有入组儿童均行v-EEG监测,应用SPSS 25.0统计学软件对v-EEG结果进行统计学分析。结果RVC组与对照组的v-EEG异常率比较差异无统计学意义(P=0.871)。缺氧组、非缺氧组和对照组的组间v-EEG异常率比较差异无统计学意义(P=0.886)。结论v-EEG异常情况不能作为RVC患儿的诊断依据,但对RVC患儿的鉴别具有一定的临床价值。
文摘为探索大脑与视觉之间的联系,提高大脑活动重建视频的清晰度与准确性,提出了一种名为高质量脑电视频重建(high quality electroencephalogram video reconstruction,HQEEGVR)的方法进行脑电信号重建视频。首先,提出三分支脑电特征提取网络——掩蔽时空频融合网络(masking spatio-temporal frequency fusion network,MSTFFNet)从脑电信号中提取大脑活动信息,深入挖掘大脑活动变化背后的语义,同时提取时空频信息;其次,引入跨模态对比学习,对齐脑电、文本、图像特征,以便生成阶段使用;然后,提出级联视频扩散模型,具体来说,先利用稳定扩散模型以脑电特征为条件生成参考视频帧,接着以视频帧为参考,融入运动矢量,引入视频扩散模型捕捉视频时间特征;最终生成高质量视频。结果表明,该模型在重建视频的主体、动作、颜色、语义等方面表现较好。可见利用脑电信号可以捕获大脑活动的视觉与语义信息,从而重建高保真度和视觉真实性的视频。
文摘The neonatal burst suppression is a severe EEG pattern and always demonstrates serious damage of nerve system. But the outcome of these patients depends on the different etiology. A total of 256 cases of video EEG recordings were analyzed in order to summarize the etiology and outcome of burst suppression. The results showed that some patients in all 17 cases of burst suppression showed EEG improvement. The etiology was the dominant factor in long term outcome. It was suggested that effective video EEG monitoring is helpful for etiologic study and prognosis evaluation.
基金Supported by the National Natural Science Foundation of China (No.90208003, 30200059) and 973 Project (No. 2003CB716106)
文摘By the means of computing approximate entropy (ApEn) of video-EEG from some clinical epileptic, ApEn of EEG with epileptiform discharges is found significantly different from that of EEG without epileptiform discharges, (p=0.002). Meanwhile, dynamic ApEn shows consistent change of EEG signal with discharges of epileptic waves inside. These results suggest that ApEn may be a useful tool for automatic recognition and detection of epileptic activity and for understanding epileptogenic mechanism.
