发表一篇学和医学成像类SCI论文
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Abstract:
BACKGROUND:Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications. NEW METHODS:Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance. RESULTS:The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31%) by integrating their complementary properties, compared to using EEG (65.52%) or fNIRS alone (58.62%). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD. COMPARISON WITH EXISTING METHODS:Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population. CONCLUSIONS:These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.
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最新影响因子:2.987 | 期刊ISSN:0165-0270 | CiteScore:2.81 |
出版周期:Semimonthly | 是否OA:YES | 出版年份:1979 |
期刊官方网址:http://www.elsevier.com/wps/find/journaldescription.cws_home/506079/authorinstructions
自引率:3.20% | 研究方向:医学-神经科学 |
出版地区:NETHERLANDS |
SCI期刊coverage:Science Citation Index Expanded(科学引文索引扩展)
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The Journal of Neuroscience Methods publishes research papers and a limited number of broad and critical reviews dealing with new methods or significant developments of recognised methods, used to investigate the organisation and fine structure, biochemistry, molecular biology, histo- and cytochemistry, physiology, biophysics and pharmacology of receptors, neurones, synapses, and glial cells, in the nervous system of man, vertebrates and invertebrates, or applicable to the clinical and behavioural sciences, tissue culture, neurocommunications, biocybernetics or computer software. Although articles should be written in sufficient detail to allow others to verify these methods, they should also be intelligible to a broad scientific audience. In addition, the journal will publish letters in a camera-ready format containing comments or discussion of methodology described in this journal, or any other journal devoted to the neurosciences. Articles should be submitted to the Editor-in-Chief. Submission of a paper to the Journal of Neuroscience Methods implies that it is not being submitted for publication elsewhere.
《神经科学杂志》上发表研究论文方法和数量有限的广泛和重要评论处理新方法或认可的重要发展方法,用于研究组织和精细结构,生物化学、分子生物学、组织和细胞化学,生理学、生物物理学和药理学的受体,神经元、突触、神经胶质细胞,人的神经系统,脊椎动物和无脊椎动物,或适用于临床及行为科学、组织培养、神经通讯、生物控制论或电脑软件。虽然文章应该写得足够详细,以使其他人能够验证这些方法,但它们也应该为广大的科学读者所理解。此外,该杂志还将以可拍照的格式出版信件,其中包括对该杂志或任何其他专门研究神经科学的杂志中所述方法的评论或讨论。文章应提交给总编。发表在《神经科学方法杂志》上的一篇论文意味着它不会在其他地方发表。
大类(学科) | 小类(学科) | 学科排名 |
医学 |
BIOCHEMICAL RESEARCH METHODS(生化研究方法) 3区 NEUROSCIENCES(神经系统科学) 4区 |
31/79 154/261 |
年度总发文量 | 年度论文发表量 | 年度综述发表量 |
202 | 200 | 2 |
引文计数(2018)
文献(2015-2017)
2552次引用
907篇文献
序号 | 类别 | 排名 | 百分位 |
1 |
大类(学科):Neuroscience
小类(学科):General Neuroscience
|
#42/111
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研究方向:生命科学 神经科学 认知科学 心理学
审稿时间: 3个月内
影响因子:3.467
ISSN:0738-3991
研究方向:医学-公共卫生、环境卫生与职业卫生
影响因子:1.512
ISSN:1991-3761
研究方向:ENGINEERING, MULTIDISCIPLINARY-ENGINEERING, MANUFA
影响因子:1.867
ISSN:1059-7123
研究方向:工程技术-计算机:人工智能
影响因子:4.418
ISSN:0894-4393
研究方向:社会科学-计算机:跨学科应用
影响因子:1.072
ISSN:0033-5177
研究方向:管理科学-统计学与概率论
影响因子:1.109
ISSN:0308-0188
研究方向:综合性期刊-综合性期刊
影响因子:1.782
ISSN:1525-822X
研究方向:
影响因子:1.322
ISSN:0018-7259
研究方向:
影响因子:0.478
ISSN:1369-1465
研究方向:
发表一篇学和医学成像类SCI论文
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