Revisiting ICEEMDAN and EEG rhythms. (July 2021)

• Record Type: Journal Article
• Authors: Thuraisingham, R.A.
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Improved complete ensemble empirical mode decomposition with adaptive noise -- Intrinsic mode functions -- Marginal Hilbert spectrum -- EEG signals; 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.2021.102701
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Mode mixing amongst its components in empirical mode decomposition minimized. Improves on the results of ICEEMDAN. Procedure tested on synthetic and electroencephalogram signals. Association of components with brain rhythms. An alternate method to process EEG data. Abstract: A new procedure is proposed to choose the optimum noise amplitude in the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method. This new procedure is referred to as the Performance Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (PICEEMDAN). The intrinsic mode functions (IMF) obtained using ICEEMDAN depend on the chosen noise amplitude. Depending on the noise amplitude, the number of IMF's can vary and also its properties. The paper explores the best choice of the noise amplitude that allows minimal mode mixing amongst its components. This involves the use of the marginal Hilbert spectrum, to compute the spectral energy over the mode's bandwidth for each IMF and a parameter defined as the total index of overlap in the frequency domain (TIOF). The procedure is demonstrated initially using synthetic signals and later applied to electroencephalogram (EEG) signals. The results indicate a marked reduction in mode mixing amongst in components. The IMF's obtained from single channel EEG data using this technique showed remarkable correspondence with the beta, alpha, theta and delta EEG rhythms. This was demonstrated using eyes open, eyes closed and seizure data and provides an alternate methodology to process EEG data.