EEG-based classification of Alzheimer’s disease and frontotemporal dementia: a comprehensive analysis of discriminative features

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Mehran Rostamikia, Yashar Sarbaz, Somaye Makouei
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Abstract

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are two main types of dementia. These diseases have similar symptoms, and they both may be considered as AD. Early detection of dementia and differential diagnosis between AD and FTD can lead to more effective management of the disease and contributes to the advancement of knowledge and potential treatments. In this approach, several features were extracted from electroencephalogram (EEG) signals of 36 subjects diagnosed with AD, 23 FTD subjects, and 29 healthy controls (HC). Mann–Whitney U-test and t-test methods were employed for the selection of the best discriminative features. The Fp1 channel for FTD patients exhibited the most significant differences compared to AD. In addition, connectivity features in the delta and alpha subbands indicated promising discrimination among these two groups. Moreover, for dementia diagnosis (AD + FTD vs. HC), central brain regions including Cz and Pz channels proved to be determining for the extracted features. Finally, four machine learning (ML) algorithms were utilized for the classification purpose. For differentiating between AD and FTD, and dementia diagnosis, an accuracy of 87.8% and 93.5% were achieved respectively, using the tenfold cross-validation technique and employing support vector machines (SVM) as the classifier.

Abstract Image

基于脑电图的阿尔茨海默病和额颞叶痴呆症分类:判别特征的综合分析
阿尔茨海默病(AD)和额颞叶痴呆(FTD)是痴呆症的两种主要类型。这两种疾病的症状相似,都可被视为阿兹海默症。痴呆症的早期检测以及 AD 和 FTD 的鉴别诊断可以更有效地控制疾病,并有助于知识的进步和潜在治疗方法的开发。本研究从 36 名被诊断为 AD 的受试者、23 名 FTD 受试者和 29 名健康对照者(HC)的脑电图(EEG)信号中提取了一些特征。在选择最佳鉴别特征时,采用了曼-惠特尼 U 检验法和 t 检验法。FTD患者的Fp1通道与AD相比差异最大。此外,δ和α子带的连通性特征也显示出这两组患者之间有很好的区分度。此外,对于痴呆诊断(AD + FTD vs. HC),包括 Cz 和 Pz 通道在内的中心脑区被证明对提取的特征具有决定性作用。最后,四种机器学习(ML)算法被用于分类目的。使用十倍交叉验证技术和支持向量机(SVM)作为分类器,在区分 AD 和 FTD 以及痴呆诊断方面,准确率分别达到了 87.8% 和 93.5%。
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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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