Kim Donghyeon, Kim Kiseon
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:352-355. doi: 10.1109/EMBC.2018.8512231.
Electroencephalogram (EEG) signal based early diagnosis of Alzheimer's Disease (AD), especially a discrimination between healthy control (HC) and mild cognitive impairment (MCI) has received remarkable attentions to complement conventional diagnosing methods in clinical fields. A relative power (RP) metric which quantifies the abnormal EEG pattern 'slowing' has widely been used as a major feature to distinguish HC and MCI, however, the optimal spectral ranges of the RP are influenced by the given dataset. In this study, we proposed the deep neural network based classifier using the RP to fully exploit and recombine the features through its own learning structure. The DNN enhanced the diagnosis results compared to shallow neural network, and enabled to interpret the results as we used the wellknown RP features as the domain knowledge. We investigated and explored the potentials of DNN based detection of the earlystage AD.
基于脑电图（EEG）信号的阿尔茨海默病（AD）早期诊断，尤其是健康对照（HC）与轻度认知障碍（MCI）之间的鉴别，在临床领域中已受到显著关注，以补充传统诊断方法。一种量化脑电图异常模式“减慢”的相对功率（RP）指标已被广泛用作区分HC和MCI的主要特征，然而，RP的最佳频谱范围受给定数据集的影响。在本研究中，我们提出了基于深度神经网络的分类器，该分类器使用RP通过自身的学习结构充分利用和重组特征。与浅层神经网络相比，深度神经网络提高了诊断结果，并且由于我们使用众所周知的RP特征作为领域知识，所以能够对结果进行解释。我们研究并探索了基于深度神经网络检测早期AD的潜力。
