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支持特征分类法对脑异常活动的分类
Support Feature Machine for Classification of Abnormal Brain Activity |
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| 课程网址: |
http://videolectures.net/kdd07_chaovalitwongse_sfmc/
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| 主讲教师: |
Art Chaovalitwongse
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| 开课单位: |
佛罗里达大学
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| 开课时间: |
2007-08-14
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| 课程语种: |
英语
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| 中文简介: |
在本研究中, 提出了一种新的多维时间序列分类技术, 即支持特征机 (sfm)。sfm 受支持向量机优化模型和最近邻域规则的启发, 将多维时间序列数据的空间和时间结合起来。本文还介绍了 sfm 在检测大脑异常活动中的应用。癫痫是本研究的一个例子。在癫痫研究中, 以多维时间序列形式获得的脑电图 (eeg) 传统上被用作捕捉大脑电变化的黄金标准工具。从多维脑电图时间序列数据中, sfm 被用来识别癫痫发作前游标, 并检测癫痫发作易感性 (癫痫发作前) 期。经验结果表明, sfm 通过5倍交叉验证, 平均对10例癫痫发作脑电图进行了80% 以上的正确分类。所提出的 sfm 优化模型非常紧凑, 具有可扩展性, 可以作为一种在线算法来实现。这项研究的结果表明, 有可能构建一个计算机算法, 用于检测癫痫发作前的游标和警告即将到来的癫痫发作通过脑电图分类。
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| 课程简介: |
In this study, a novel multidimensional time series classification technique, namely support feature machine (SFM), is proposed. SFM is inspired by the optimization model of support vector machine and the nearest neighbor rule to incorporate both spatial and temporal of the multi-dimensional time series data. This paper also describes an application of SFM for detecting abnormal brain activity. Epilepsy is a case in point in this study. In epilepsy studies, electroencephalograms (EEGs), acquired in multidimensional time series format, have been traditionally used as a gold-standard tool for capturing the electrical changes in the brain. From multi-dimensional EEG time series data, SFM was used to identify seizure pre-cursors and detect seizure susceptibility (pre-seizure) periods. The empirical results showed that SFM achieved over 80% correct classification of per-seizure EEG on average in 10 patients using 5-fold cross validation. The proposed optimization model of SFM is very compact and scalable, and can be implemented as an online algorithm. The outcome of this study suggests that it is possible to construct a computerized algorithm used to detect seizure pre-cursors and warn of impending seizures through EEG classification.
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| 关 键 词: |
支持功能机异常脑活动的分类; 脑电图（EEG）; 交叉验证
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-04：毛岱琦(课程编辑志愿者)
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| 阅读次数: |
20
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