利用脑电信号检测自闭症谱系障碍的基于特征选择的混合相似性和级联深度最大值模糊网络

IF 2.6 4区 生物学 Q2 BIOLOGY
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引用次数: 0

摘要

自闭症谱系障碍(ASD)是一种影响人的理解能力和行为方式的神经系统疾病。自闭症是一种终生残疾,迄今为止任何疗法都无法完全治疗自闭症。然而,及时发现和持续治疗对自闭症患者有着巨大的作用。现有的模式需要很长时间才能确诊,而且将自闭症与各种发育障碍区分开来也非常复杂。为了通过及时干预促进早期诊断,节约医疗成本,并从长远角度减轻家庭压力,本研究利用脑电图和深度学习模型引入了一种经济实惠、简单明了的诊断模型来检测自闭症。本文提出了一种名为级联深度最大值模糊网络(Cascade DMFN)的混合深度学习模型来识别 ASD,它是通过整合深度最大值网络(DMN)和混合级联神经模糊来实现的。此外,还采用了堪培拉距离(Canberra distance)和库马-哈斯布鲁克(Kumar-hassebrook)等混合相似度量来进行特征选择技术。此外,还使用脑电图数据集和 BCIAUT_P300 数据集来分析所设计的用于检测自闭症谱系障碍的级联 DMFN。所设计的级联 DMFN 的准确率为 0.930,负预测值(NPV)为 0.919,正预测值(PPV)为 0.923,真阴性率(TNR)为 0.926,真阳性率(TPR)为 0.934,优于其他经典模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid similarity based feature selection and cascade deep maxout fuzzy network for Autism Spectrum Disorder detection using EEG signal

Autism Spectrum Disorder (ASD) is a neurological disorder that influences a person’s comprehension and way of behaving. It is a lifetime disability that cannot be completely treated using any therapy up to date. Nevertheless, in time identification and continuous therapies have a huge effect on autism patients. The existing models took a long time to confirm the diagnosis process and also, it is highly complex to differentiate autism from various developmental disorders. To facilitate early diagnosis by providing timely intervention, saving healthcare costs and reducing stress for the family in the long run, this research introduces an affordable and straightforward diagnostic model to detect ASD using EEG and deep learning models. Here, a hybrid deep learning model called Cascade deep maxout fuzzy network (Cascade DMFN) is proposed to identify ASD and it is achieved by the integration of Deep Maxout Network (DMN) and hybrid cascade neuro-fuzzy. Moreover, hybrid similarity measures like Canberra distance and Kumar-hassebrook is employed to conduct the feature selection technique. Also, the EEG dataset and BCIAUT_P300 dataset are used for analyzing the designed Cascade DMFN for detecting Autism Spectrum Disorder. The designed Cascade DMFN has outperformed other classical models by yielding a high accuracy of 0.930, Negative Predictive Value (NPV) of 0.919, Positive Predictive Value (PPV) of 0.923, True Negative Rate (TNR) of 0.926, and True Positive Rate (TPR) of 0.934.

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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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