一种进化级联神经网络睡眠脑电净化技术
An Evolving Cascade Neural Network Technique for Cleaning Sleep Electroencephalograms
摘要：
描述了进化级联神经网络（ECNNs）和一种新的能够选择信息特征的训练算法。ECNN最初使用一个输入节点进行学习，然后通过添加新的输入和新的隐藏神经元来进化。所得到的ECNN具有接近最小数量的隐藏神经元和输入。该算法被成功地用于训练ECNN识别伪迹睡眠脑电图（EEG），这是视觉标记的EEG观众。在我们的实验中，ECNN优于标准神经网络以及进化技术。
英文摘要：
Evolving Cascade Neural Networks (ECNNs) and a new training algorithm capable of selecting informative features are described. The ECNN initially learns with one input node and then evolves by adding new inputs as well as new hidden neurons. The resultant ECNN has a near minimal number of hidden neurons and inputs. The algorithm is successfully used for training ECNN to recognise artefacts in sleep electroencephalograms (EEGs) which were visually labelled by EEG-viewers. In our experiments, the ECNN outperforms the standard neural-network as well as evolutionary techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
