Automatic Artifact Reduction Based on MEMD- for Seizure Prediction
Automatic Artifact Reduction Based on MEMD- for Seizure Prediction
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基于 MEMD 的自动伪影减少 - 用于癫痫发作预测
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Xiaobo Wu
中科院分区：
文献类型：
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作者：
Lihan Tang;Menglian Zhao;Yizhao Zhou;Xiaobo Wu
癫痫发作预测的性能通常受到各种伪影的影响，尤其是生理伪影。为了提高癫痫发作预测的性能，提出了一种基于多元经验模态分解和独立分量分析（MEMD-ICA）的伪影自动降噪方法。该方法既能准确识别眼电信号和肌电信号伪影，又能尽可能保留有用的神经信号。基于CHB-MIT数据库的癫痫发作预测准确率为90.59%，灵敏度为91.09%。
The performance of seizure prediction is usually affected by various kinds of artifacts, especially by physiological artifacts. To improve the performance of seizure prediction, this paper proposed an automatic artifact reduction method based on multivariate empirical mode decomposition and independent component analysis (MEMD-ICA). The proposed method could identify electrooculography (EOG) and electromyographic (EMG) artifacts precisely while keeping the useful neural signals as much as possible. The performance of seizure prediction has been significantly improved with an accuracy of 90.59% and a sensitivity of 91.09% based on CHB-MIT database.