Combining EEG signal processing with supervised methods for Alzheimer's patients classification.
Combining EEG signal processing with supervised methods for Alzheimer's patients classification.
复制标题
DOI：
10.1186/s12911-018-0613-y
复制
发表时间：
2018-05-31
影响因子：
3.5
通讯作者：
De Cola MC
中科院分区：
文献类型：
--
作者：
Fiscon G;Weitschek E;Cialini A;Felici G;Bertolazzi P;De Salvo S;Bramanti A;Bramanti P;De Cola MC
阿尔茨海默病（AD）是一种以进行性痴呆为特征的神经退行性疾病，实际上没有治愈方法。早期检测受AD影响的患者可以通过分析他们的脑电图（EEG）信号来获得，这些信号显示出复杂性的降低、同步性的扰动和节奏的减慢。在这项工作中，我们应用的程序，利用特征提取和分类技术的EEG信号，其目的是区分受AD影响的患者受轻度认知障碍（MCI）和健康对照（HC）样本。具体来说，我们通过应用傅立叶变换和小波变换对属于AD，MCI和HC类的109个样本进行时频分析。分类过程设计有以下步骤：（i）EEG信号的预处理;（ii）通过离散傅立叶变换和小波变换进行特征提取;以及（iii）使用基于树的监督方法进行分类。通过应用我们的程序，我们能够提取可靠的人类可解释的分类模型，允许自动将患者分配到其所属的类别。特别是，通过利用小波特征提取，我们实现了83%，92%和79%的准确性时，处理HC与AD，HC与MCI，MCI与AD分类问题，分别。最后，通过比较两种特征提取方法的分类性能，我们发现小波分析优于傅立叶。因此，我们建议它结合监督的方法，自动患者分类的基础上，他们的EEG信号，以帮助医疗诊断痴呆症。
Alzheimer’s Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms. In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods. By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively. Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.
登录
查看更多内容
DOI：
10.3390/s130912431
发表时间：
2013-09-17
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Chowdhury RH;Reaz MB;Ali MA;Bakar AA;Chellappan K;Chang TG
通讯作者：
Chang TG
影响因子：
4.6
作者：
Davidson, Paul R.;Jones, Richard D.;Peiris, Malik T. R.
通讯作者：
Peiris, Malik T. R.
影响因子：
6.4
作者：
Bertolazzi, P.;Felici, G.;Weitschek, E.
通讯作者：
Weitschek, E.
影响因子：
2.1
作者：
Dauwels, J.;Vialatte, F.;Cichocki, A.
通讯作者：
Cichocki, A.
DOI：
10.1109/jbhi.2013.2249590
发表时间：
2013-05-01
影响因子：
7.7
作者：
Al-Timemy, Ali H.;Bugmann, Guido;Outram, Nicholas
通讯作者：
Outram, Nicholas