Scalp EEG-based Classification of Disorder of Consciousness States using Machine Learning Techniques

Raveendran, Shangari K.; Kumar, S. Pravin; Kenchiah, Raghavendra; Farsana, M K; Choudary, Ravindranath; Bansal, Sonia; Binu, V S; Ramakrishnan, A. G.; Ramakrishnan, Sivasubramanian; Kala, S

doi:10.1109/esdc56251.2023.10149867

Cited by 1 publication

(2 citation statements)

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“…The electrode placement during recording followed the international 10/20 system at a sampling rate of 256 Hz, with reference electrodes placed on the left and right ear lobes (A1, A2), maintaining an impedance below 5K Ohm. The steps followed for EEG recording are as per the work (Raveendran et al, 2023 ).…”

Section: Methodology and Data Collectionmentioning

confidence: 99%

“…Since some channels were unavailable in the EEG records of some coma patients; to maintain uniformity in the data for further analysis, the number of channels was reduced to 17 (F3, Fz, F4, F7, F8, Cz, C3, C4, T3, T4, T5, T6, P3, Pz, P4, O1, and O2). The basic analysis of the resting state EEG of DOC patients for the classification is performed as reported in the previous work (Raveendran et al, 2023 ). The pre-processing steps were carried out using Python on the Anaconda Jupyter online platform.…”

Section: Methodology and Data Collectionmentioning

confidence: 99%

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Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness

Raveendran,

Kenchaiah,

Kumar

et al. 2024

Front. Neurosci.

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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.

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Section: Methodology and Data Collectionmentioning

confidence: 99%

Section: Methodology and Data Collectionmentioning

confidence: 99%