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通过脑电图信号进行阿尔茨海默病检测的智能数据驱动系统

Smart-Data-Driven System for Alzheimer Disease Detection through Electroencephalographic Signals.

作者信息

Araújo Teresa, Teixeira João Paulo, Rodrigues Pedro Miguel

机构信息

CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal.

CEDRI-Research Centre in Digitalization and Intelligent Robotics and UNIAG-Management Applied Research Unit, Instituto Politécnico de Bragança (IPB), Braganca, Campus de Sta Apolónia, Apartado 134, 5301-857 Bragança, Portugal.

出版信息

Bioengineering (Basel). 2022 Mar 28;9(4):141. doi: 10.3390/bioengineering9040141.

DOI:10.3390/bioengineering9040141
PMID:35447701
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9031324/
Abstract

BACKGROUND

Alzheimer's Disease (AD) stands out as one of the main causes of dementia worldwide and it represents around 65% of all dementia cases, affecting mainly elderly people. AD is composed of three evolutionary stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). It is crucial to create a tool for assisting AD diagnosis in its early stages with the aim of halting the disease progression.

METHODS

The main purpose of this study is to develop a system with the ability of differentiate each disease stage by means of Electroencephalographic Signals (EEG). Thereby, an EEG nonlinear multi-band analysis by Wavelet Packet was performed enabling to extract several features from each study group. Classic Machine Learning (ML) and Deep Learning (DL) methods have been used for data classification per EEG channel.

RESULTS

The maximum accuracies obtained were 78.9% (Healthy controls (C) vs. MCI), 81.0% (C vs. ADM), 84.2% (C vs. ADA), 88.9% (MCI vs. ADM), 93.8% (MCI vs. ADA), 77.8% (ADM vs. ADA) and 56.8% (All vs. All).

CONCLUSIONS

The proposed method outperforms previous studies with the same database by 2% in binary comparison MCI vs. ADM and central and parietal brain regions revealed abnormal activity as AD progresses.

摘要

背景

阿尔茨海默病（AD）是全球痴呆症的主要病因之一，约占所有痴呆症病例的65%，主要影响老年人。AD由三个演变阶段组成：轻度认知障碍（MCI）、轻度和中度AD（ADM）以及重度AD（ADA）。创建一种有助于早期AD诊断的工具以阻止疾病进展至关重要。

方法

本研究的主要目的是开发一个能够通过脑电图信号（EEG）区分每个疾病阶段的系统。因此，通过小波包进行了EEG非线性多频段分析，从而能够从每个研究组中提取多个特征。经典机器学习（ML）和深度学习（DL）方法已用于按EEG通道进行数据分类。

结果

获得的最高准确率分别为78.9%（健康对照（C）与MCI）、81.0%（C与ADM）、84.2%（C与ADA）、88.9%（MCI与ADM）、93.8%（MCI与ADA）、77.8%（ADM与ADA）和56.8%（所有组相互比较）。

结论

在MCI与ADM的二元比较中，所提出的方法比使用相同数据库的先前研究高出2%，并且随着AD的进展，大脑中央和顶叶区域显示出异常活动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/fdf70e06b05a/bioengineering-09-00141-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/60bf77f5f4fb/bioengineering-09-00141-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/312afb394afd/bioengineering-09-00141-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/60abf36c6105/bioengineering-09-00141-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/65e0cfc5fad9/bioengineering-09-00141-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/fdf70e06b05a/bioengineering-09-00141-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/60bf77f5f4fb/bioengineering-09-00141-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/312afb394afd/bioengineering-09-00141-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/60abf36c6105/bioengineering-09-00141-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/65e0cfc5fad9/bioengineering-09-00141-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3da4/9031324/fdf70e06b05a/bioengineering-09-00141-g005.jpg

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