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

• Published in: Computers in biology and medicine Vol. 117; p. 103596
• Main Authors: Raeisi, Khadijeh, Mohebbi, Maryam, Khazaei, Mohammad, Seraji, Masoud, Yoonessi, Ali
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2020; Elsevier Limited
• Subjects: 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
• DOI: 10.1016/j.compbiomed.2019.103596

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 address these issues, in this work, we proposed a robust pattern recognition algorithm as a computer-aided diagnosis system. This method is based on calculating the pairwise phase-synchrony of EEG recordings during a visual task. Initially, the bivariate empirical mode decomposition (BEMD) was applied to extract the intrinsic mode functions (IMFs). The phases of these IMFs were then obtained using the Hilbert transform to be utilized in the mean phase coherence (MPC), a measure for phase-synchrony calculation. After the construction of the feature space using MPC values, the ReliefF algorithm was applied for dimension reduction. Finally, the best distinguishing features were input to a k-nearest neighbor (KNN) classifier. The results revealed a higher level of network synchronization in the posterior regions of the brain and desynchronization in the anterior regions among the MS group as compared with the normal subjects. In the validation phase, the leave-one-subject-out cross-validation (LOOCV) method was used to assess the validity of the proposed algorithm. We achieved an accuracy, sensitivity, and specificity of 93.09%, 91.07%, and 95.24% for red-green, 90.44%, 88.39%, and 92.62% for luminance, and 87.44%, 87.05%, and 87.86% for blue-yellow tasks, respectively. The experimental results demonstrated the reliability of the presented method to be generalized in the field of automated MS diagnosis systems. •The presented research is a step forward in the development of automated MS diagnosis systems using event-related EEGs.•Phase-synchrony information derived from bivariate empirical mode decomposition was used for MS diagnosis.•.Higher levels of networks synchronization in the posterior regions of the brain were seen among the MS group.