An Empirical Mode Decomposition approach for automated diagnosis of migraine. (March 2022)

• Record Type: Journal Article
• Authors: Aslan, Zülfikar
• Is Part Of: Biomedical signal processing and control. Volume 73(2022)
• Publication Date: 2022-03
• Subjects: Migraine detection -- EMD -- Ensemble classifiers -- EEG; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103413
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The proposed method presents a study with an EMD approach aimed at automatically diagnosing migraine from EEG signals. Testing the classification performance for electrode-based migraine diagnosis with the proposed method. Testing the distinctiveness of the obtained features by applying different statistical tests (Kruskal Wallis-Wilcoxon). Comparison of the computational complexity of the proposed method with different decomposition methods. Increasing classification performance in EEG-based migraine diagnosis by considering existing CAD studies. Abstract: This study presents an Empirical Mode Decomposition (EMD) approach that aims to automatically detect migraine disease (MD) from electroencephalogram (EEG) recordings of migraine patient (MP) and healthy control (HC) subjects. First, the Multiscale Principal Component Analysis (MSPCA) method was applied to remove the noise on the raw EEG signals. Later, EEG signals were separated into intrinsic mode functions (IMF) components by EMD method. Statistical features were calculated and extracted from each IMF component. By applying the Kruskal Wallis (KW) test, the ability to distinguish these features in classification was tested. Classification performances are tested by classifying the features of each IMF component with a few leading ensemble algorithms. The highest classification accuracy of 92.47% was achieved by classifying the features of the IMF1 component with the Random Forest learning algorithm. At the last stage of the study, a comparative analysis with different time–frequency analysis methods is presented. As a result of the experimental comparison of our proposed method, it has been observed that it has a higher classification performance than other studies that detect EEG-based MD. With these aspects, our study reveals that it has the potential to be used as a computer aided diagnosis system that will support expert opinion in the detection of MD.