论文标题
python（深度学习和机器学习）用于识别酒精中毒疾病的例子
Python (deep learning and machine learning) for EEG signal processing on the example of recognizing the disease of alcoholism
论文作者
论文摘要
酒精中毒是世界上最常见的疾病之一。这种类型的药物滥用导致对含乙醇饮料的精神和身体依赖。酒精中毒伴随着对人格的逐步退化和内部器官的损害。今天仍然不存在一种快速诊断方法来检测这种疾病。本文介绍了通过神经网络快速而匿名的酒精中毒诊断的方法。对于这种方法，不需要有关该主题的任何私人信息。对于实施，我们考虑了机器学习和深神网络的各种算法。详细分析了神经网络来自电极信号的相关性。小波变换和快速的傅立叶变换被考虑。手稿表明，仅使用脑电图相关信号数据集运行的深神经网络可以匿名以高精度对酒精和对照组进行分类。一方面，这种方法将使受试者在没有任何个人数据的情况下对酒精中毒进行测试，这不会给受试者带来不便或羞耻，另一方面，受试者将无法欺骗诊断为疾病存在的受试者的专家。
Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don't need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy. On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specialists who diagnose the subject for the presence of the disease.
