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首页> 外文期刊>Clinical EEG and neuroscience: official journal of the EEG and Clinical Neuroscience Society (ENCS) >Improving Alzheimer's disease diagnosis with machine learning techniques.
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Improving Alzheimer's disease diagnosis with machine learning techniques.

机译：通过机器学习技术改善阿尔茨海默氏病的诊断。

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摘要

There is not a specific test to diagnose Alzheimer's disease (AD). Its diagnosis should be based upon clinical history, neuropsychological and laboratory tests, neuroimaging and electroencephalography (EEG). Therefore, new approaches are necessary to enable earlier and more accurate diagnosis and to follow treatment results. In this study we used a Machine Learning (ML) technique, named Support Vector Machine (SVM), to search patterns in EEG epochs to differentiate AD patients from controls. As a result, we developed a quantitative EEG (qEEG) processing method for automatic differentiation of patients with AD from normal individuals, as a complement to the diagnosis of probable dementia. We studied EEGs from 19 normal subjects (14 females/5 males, mean age 71.6 years) and 16 probable mild to moderate symptoms AD patients (14 females/2 males, mean age 73.4 years. The results obtained from analysis of EEG epochs were accuracy 79.9% and sensitivity 83.2%. The analysis considering the diagnosis of each individual patient reached 87.0% accuracy and 91.7% sensitivity.
机译：没有诊断阿尔茨海默氏病（AD）的特定测试。其诊断应基于临床病史，神经心理学和实验室检查，神经影像学和脑电图（EEG）。因此，需要新的方法来实现更早，更准确的诊断并跟踪治疗结果。在这项研究中，我们使用了一种名为支持向量机（SVM）的机器学习（ML）技术来搜索EEG时代的模式，以区分AD患者与对照患者。结果，我们开发了一种定量脑电图（qEEG）处理方法，用于自动区分AD患者与正常个体，作为对可能的痴呆症诊断的补充。我们研究了来自19名正常受试者（14名女性/ 5名男性，平均年龄71.6岁）和16名可能出现轻度至中度症状的AD患者（14名女性/ 2名男性，平均年龄73.4岁）的脑电图。从脑电图时代的分析中得出的结果是准确的分别为79.9％和83.2％，考虑到每个患者的诊断，分析的准确率分别为87.0％和91.7％。

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