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Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt–Jakob Disease for Assisting Early Diagnosis

In: Computational Neuroscience

Author

Listed:
  • Chih-I Hung

    (National Yang-Ming University
    Veterans General Hospital)

  • Po-Shan Wang

    (The Neurological Institute, Taipei Municipal Gan-Dau Hospital
    National Yang-Ming University School of Medicine
    The Neurological Institute, Taipei Veterans General Hospital)

  • Bing-Wen Soong

    (National Yang-Ming University School of Medicine
    The Neurological Institute, Taipei Veterans General Hospital)

  • Shin Teng

    (National Yang-Ming University
    Veterans General Hospital)

  • Jen-Chuen Hsieh

    (Veterans General Hospital
    Institute of Brain Science, National Yang-Ming University)

  • Yu-Te Wu

    (National Yang-Ming University
    Institute of Brain Science, National Yang-Ming University)

Abstract

Creutzfeldt–Jakob disease (CJD) is a rare, transmissible, and fatal prion disorder of brain. Typical electroencephalography (EEG) patterns, such as the periodic sharp wave complexes (PSWCs), do not clearly emerge until the middle stage of CJD. To reduce transmission risks and avoid unnecessary treatments, the recognition of the hidden PSWCs’ forerunners from the contaminated EEG signals in the early stage is imperative. In this study, independent component analysis (ICA) was employed on the raw EEG signals recorded at the first admissions of five patients to segregate the co-occurrence of multiple disease-related features, which were difficult to be detected from the smeared EEG. Clear CJD-related waveforms, i.e., frontal intermittent rhythmical delta activity (FIRDA), fore PSWCs (triphasic waves), and periodic lateralized epileptiform discharges (PLEDs), have been successfully and simultaneously resolved from all patients. The ICA results elucidate the concurrent appearance of FIRDA and PLEDs or triphasic waves within the same EEG epoch, which has not been reported in the previous literature. Results show that ICA is an objective and effective means to extract the disease-related patterns for facilitating the early diagnosis of CJD.

Suggested Citation

  • Chih-I Hung & Po-Shan Wang & Bing-Wen Soong & Shin Teng & Jen-Chuen Hsieh & Yu-Te Wu, 2010. "Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt–Jakob Disease for Assisting Early Diagnosis," Springer Optimization and Its Applications, in: Wanpracha Chaovalitwongse & Panos M. Pardalos & Petros Xanthopoulos (ed.), Computational Neuroscience, chapter 0, pages 57-74, Springer.
  • Handle: RePEc:spr:spochp:978-0-387-88630-5_4
    DOI: 10.1007/978-0-387-88630-5_4
    as

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