Application of variational mode decomposition in automated migraine disease diagnosis

The clinical diagnosis of migraine if supplanted by the modality of the electroencephalograph signals may help in delineating the neural correlates, management and prognosis of the disease progress. Recent advances in the area of biomedical signal processing have led to the development of various feature extraction and classification techniques for multi-resolution analysis of electroencephalograph signals and diagnosis of diseased conditions. In the present work, a methodology using variational mode decomposition is proposed for migraine disease diagnosis from electroencephalogram signals. In the proposed methodology, variational mode decomposition is employed for decomposing electroencephalogram signals into number of modes. Sample entropy and Higuchi's fractal dimension are estimated from the decomposed modes as features and three soft computing techniques viz. neural network, support vector machine and random forest are used for classifying extracted features. Classification results obtained from soft computing techniques indicated that the proposed methodology effectively identified migraine patients using electroencephalogram data.