Raveendran, S and Kenchaiah, R and Kumar, S and Sahoo, J and Farsana, MK and Chowdary Mundlamuri, R and Bansal, S and Binu, VS and Ramakrishnan, AG and Ramakrishnan, S and Kala, S (2024) Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness. In: Frontiers in Neuroscience, 18 .
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Abstract
Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5 for multi-class classification (the best in the literature) and 86.7 for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making. Copyright © 2024 Raveendran, Kenchaiah, Kumar, Sahoo, Farsana, Chowdary Mundlamuri, Bansal, Binu, Ramakrishnan, Ramakrishnan and Kala.
Item Type: | Journal Article |
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Publication: | Frontiers in Neuroscience |
Publisher: | Frontiers Media SA |
Additional Information: | The copyright for this article belongs to author. |
Keywords: | acute disseminated encephalomyelitis; adult; algorithm; alpha rhythm; arousal; Article; awareness; bacterial meningitis; binary classification; brain; brain disease; brain hemorrhage; brain toxicity; central nervous system lupus; cerebral sinus thrombosis; cerebrovascular accident; clinical article; consciousness; consciousness disorder; controlled study; decision making; decision tree; decomposition; demyelination; diffusion kurtosis imaging; electric potential; electroencephalogram; entropy; feature extraction; female; Glasgow coma scale; human; hypoxia; hypoxic ischemic encephalopathy; intensive care unit; Kruskal Wallis test; learning algorithm; machine learning; male; meningioma; meningoencephalitis; minimally conscious state; patient care; persistent vegetative state; resource management; traumatic brain injury |
Department/Centre: | Division of Biological Sciences > Centre for Neuroscience Division of Electrical Sciences > Electrical Engineering |
Date Deposited: | 15 May 2024 05:19 |
Last Modified: | 15 May 2024 05:19 |
URI: | https://eprints.iisc.ac.in/id/eprint/84482 |
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