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DOI码：10.1109/ICOSP.2014.7015176

发表刊物：2014 12th International Conference on Signal Processing (ICSP)

刊物所在地：UNITED STATES

关键字：Feature extraction, Correlation, Training, Algorithm design and analysis, Heuristic algorithms, Blood, Accuracy

摘要：The functional connectivity of brain is a key point of brain network analysis. The BOLD (blood oxygen level dependent) fMRI (functional magnetic resonance imaging) signal is an effective projection signal of brain function. A dynamic method in resting-state (RS) functional connectivity analysis of brains is proposed in this paper. In contrast to traditional static method, a sliding window is used to separate whole period RS-BOLD signal into variable segments in time domain to rebuild a dynamic set of RS-BOLD and enlarge the sample size. It will enable the utilization of neural network classifier or other machine learning algorithms to analyze features and patterns. By training module from features extracted from brain network of glioma patients and normal people, it states 100% accuracy in glioma diagnosis. Besides, this dynamic analysis method also extracts 124 feature connections of glioma brain network with 70% confidence coefficient. By comparison, we also exploit brain network using general graph-based static method. It fails to reveal significant alternations between glioma and normal.

合写作者：Ziyi Wang, Weibei Dou Xue Wang, Min Li, Mingyu Zhang, Hongyan Chen, Shaowu Li, Jianping Dai,窦维蓓

第一作者：Wenbo Zhang,高宇

论文类型：会议论文

通讯作者：Weibei Dou,窦维蓓

ISSN号：2164-5221

是否译文：否

发表时间：2014-10-19