Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer’s disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer’s disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: -nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer’s disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer’s disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the -nearest neighbors’ approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.

中文翻译：

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使用脑电图信号通过时间依赖性功率谱描述符和卷积神经网络诊断阿尔茨海默病


使用获得早期症状一致的生物标志物的策略，早期发现阿尔茨海默病及其并发症至关重要。脑电图是一项技术，可以通过突触后电位记录大脑皮层电活动持续时间内具有相同空间方向的数千个神经元。因此，本文使用时间相关功率谱描述符来诊断三组：轻度认知障碍、阿尔茨海默病和健康对照测试样本的脑电图信号功能。记录传统分类方法三种模式下最终使用的特征： -最近邻、支持向量机、线性判别分析方法和记录结果。最后，针对阿尔茨海默病患者分类，提出了卷积神经网络架构。结果通过输出评估来表示。对于卷积神经网络方法来说，准确率的准确含义是82.3%。 85%的轻度认知障碍病例被准确地深入检测，但89.1%的阿尔茨海默病和75%的健康人群被正确诊断。所提出的卷积神经网络优于其他方法，因为性能和-最近邻居的方法是下一个目标。线性判别分析和支持向量机位于曲线值下方的低区域。