Role of EEG as biomarker in the early detection and classification of dementia.
Role of EEG as biomarker in the early detection and classification of dementia.
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DOI：
10.1155/2014/906038
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发表时间：
2014
影响因子：
--
通讯作者：
Escudero J
中科院分区：
文献类型：
--
作者：
Al-Qazzaz NK;Ali SH;Ahmad SA;Chellappan K;Islam MS;Escudero J
痴呆症的早期检测和分类是医生定制患者治疗方案以更好地管理这些疾病的发展和进展的重要临床支持任务。正在努力在早期阶段诊断这些神经退行性疾病。事实上，早期诊断有助于患者在出现明显的智力下降之前获得最大的治疗益处。脑电图作为一种检测脑活动变化和临床诊断的工具，由于其在量化痴呆症脑变性变化方面的能力而变得越来越受欢迎。本文综述了脑电图作为一种基于信号处理的生物标志物在早期痴呆诊断和严重程度分类中的作用。该综述首先讨论了痴呆类型和认知谱，然后介绍了有效的预处理去噪以消除可能的伪影。它继续与特征提取的描述，通过使用线性和非线性技术，它结束时，大量的各种分离技术进行分类EEG信号的简要说明。本文还从最流行的研究中提供了一个想法，可能有助于早期诊断痴呆症，并通过脑电图信号处理和分析进行分类。
The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.
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DOI：
10.1016/s0140-6736(20)32205-4
发表时间：
2021-04-24
期刊：
Lancet (London, England)
影响因子：
--
作者：
Scheltens P;De Strooper B;Kivipelto M;Holstege H;Chételat G;Teunissen CE;Cummings J;van der Flier WM
通讯作者：
van der Flier WM
在阿尔茨海默氏病中循环的microRNA：寻找新型生物标志物。
影响因子：
4.8
作者：
Dorval V;Nelson PT;Hébert SS
通讯作者：
Hébert SS
影响因子：
--
作者：
Berger, H
通讯作者：
Berger, H
影响因子：
6.7
作者：
BESSERVE, MICHEL;JERBI, KARIM;GARNERO, LINE
通讯作者：
GARNERO, LINE
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