Blind source separation of concurrent disease-related patterns from EEG in Creutzfeldt-Jakob disease for assisting early diagnosis

Chih I. Hung, Po Shan Wang, Bing Wen Soong, Shin Teng, Jen Chuen Hsieh, Yu Te Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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.

Original languageEnglish
Pages (from-to)2168-2179
Number of pages12
JournalAnnals of Biomedical Engineering
Volume35
Issue number12
DOIs
StatePublished - Dec 2007

Keywords

  • Frontal intermittent rhythmical delta activity (FIRDA)
  • Periodic lateralized epileptiform discharges (PLEDs)
  • Periodic sharp wave complexes (PSWCs)

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