Phase-synchrony evaluation of EEG signals for Multiple Sclerosis diagnosis based on bivariate empirical mode decomposition during a visual task

Despite the widespread prevalence of Multiple Sclerosis (MS), the study of brain interactions is still poorly understood. Moreover, there has always been a great need to automate the MS diagnosis procedure to eliminate the evaluation errors thereby improving its consistency and reliability. To addre...

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Veröffentlicht in:	Computers in biology and medicine Jg. 117; S. 103596
Hauptverfasser:	Raeisi, Khadijeh, Mohebbi, Maryam, Khazaei, Mohammad, Seraji, Masoud, Yoonessi, Ali
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.02.2020 Elsevier Limited
Schlagworte:	Algorithms Automation Bivariate analysis Bivariate empirical mode decomposition Brain Brain research Diagnosis EEG Electroencephalography Evaluation Fractals Hilbert transformation K-nearest neighbors algorithm Mathematical analysis Mean phase coherence Medical diagnosis Methods Morphology Multiple sclerosis Nervous system Noise Pattern recognition Pattern recognition systems Phase coherence Phase-synchrony Reliability analysis reliefF Signal processing Synchronism Synchronization Visual task Visual tasks Bivariate empirical mode decomposition Multiple sclerosis reliefF Electroencephalography Phase-synchrony Mean phase coherence Visual task
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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