首页|Indian Institute of Information Technology Researchers Re- port Recent Findings in Machine Learning (Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness)
Indian Institute of Information Technology Researchers Re- port Recent Findings in Machine Learning (Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness)
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2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Kerala, India, by NewsRx editors, research stated, “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.” Our news reporters obtained a quote from the research from Indian Institute of Information Technol- ogy: “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 ac- tivities 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 perfor- mance 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 classi- fication 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.”
Indian Institute of Information TechnologyKeralaIndiaAsiaCyborgsEmerging TechnologiesMachine Learning
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