基于机器学习的癫痫发作预测方法的通道选择
Channel selection for epilepsy seizure prediction method based on machine learning.
作者信息
Chang Nai-Fu, Chen Tung-Chien, Chiang Cheng-Yi, Chen Liang-Gee
机构信息
DSP/IC Design Lab, Graduate Institute of Electronics Engineering, NationalTaiwan University, Taipei, Taiwan.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5162-5. doi: 10.1109/EMBC.2012.6347156.
The studies on seizure prediction problem have shown great improvement these years. Machine learning based seizure prediction method shows great performance by doing pattern recognition on high-dimensional bivariate synchronization features. However, the computation loading of the machine learning based method may be too high to meet wearable or implantable devices with the power and area constraints. In this work, channel selection is proposed to reduce the channel number from 22 to less than 6 channels and therefore more than 93.73% of the computation loading is saved through the method. The best result shows successful rate of 60.6% in 3-channel cases of ECoG database and successful rate of 70% in 3-channel cases of EEG database.
近年来，关于癫痫发作预测问题的研究取得了显著进展。基于机器学习的癫痫发作预测方法通过对高维双变量同步特征进行模式识别，展现出了优异的性能。然而，基于机器学习的方法计算量可能过高，难以满足受功率和面积限制的可穿戴或植入式设备的需求。在这项工作中，我们提出了通道选择方法，将通道数量从22个减少到少于6个，从而通过该方法节省了超过93.73%的计算量。最佳结果显示，在脑皮质电图（ECoG）数据库的3通道情况下成功率为60.6%，在脑电图（EEG）数据库的3通道情况下成功率为70%。
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