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.
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Acknowledgements
The study was funded by the Taipei Veterans General Hospital (V96 ER1-005) and National Science Council (NSC 96-2221-E-010-003-MY3, NSC 97-2752-B-075-001-PAE, NSC 97-2752-B-010-003-PAE).
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Hung, CI., Wang, PS., Soong, BW., Teng, S., Hsieh, JC., Wu, YT. (2010). Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt–Jakob Disease for Assisting Early Diagnosis. In: Chaovalitwongse, W., Pardalos, P., Xanthopoulos, P. (eds) Computational Neuroscience. Springer Optimization and Its Applications(), vol 38. Springer, New York, NY. https://doi.org/10.1007/978-0-387-88630-5_4
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