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Classify epileptic EEG signals using weighted complex networks based community structure detection

机译:使用基于加权复杂网络的社区结构检测对癫痫性脑电信号进行分类

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摘要

Background: Epilepsy is a brain disorder that is mainly diagnosed by neurologists based on electroencephalogram (EEG) recordings. Epileptic EEG signals are recorded as multichannel signals. A reliable technique for analysing multi-channel EEG signals is in urgent demand for the treatment and diagnosis of patients who have epilepsy and other brain disorders.
机译:背景:癫痫病是一种脑部疾病,主要由神经科医生根据脑电图(EEG)记录进行诊断。癫痫性脑电信号记录为多通道信号。对于患有癫痫和其他脑部疾病的患者的治疗和诊断,迫切需要一种可靠的分析多通道EEG信号的技术。

著录项

  • 来源
    《Expert Systems with Application》 |2017年第30期|87-100|共14页
  • 作者

    Diykh Mohammed; Li Yan; Wen Peng;

  • 作者单位

    Univ Southern Queensland, Sch Agr Computat & Environm Sci, Toowoomba, Qld, Australia|Thi Qar Univ, Coll Educ Pure Sci, Nasiriyah, Iraq;

    Univ Southern Queensland, Sch Agr Computat & Environm Sci, Toowoomba, Qld, Australia|Hubei Univ Technol, Sch Elect & Elect Engn, Wuhan, Hubei, Peoples R China;

    Univ Southern Queensland, Sch Agr Computat & Environm Sci, Toowoomba, Qld, Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Epileptic EEG signals; Modularity; Statistical features; Weighted complex networks;

    机译:癫痫脑电信号;模块化;统计特征;加权复杂网络;

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