Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis.

Electroencephalography (EEG) reflects brain activity and is crucial for diagnosing states such as coma and brain-death. However, the clinical interpretation of EEG signals faces challenges due to the patients' faint brain activity and the complexities of the intensive care unit environment, further compounded by the absence of quantified standards for signal analysis. This study developed an improved denoise method tailored to the characteristics of Coma/Brain-Death EEG signals. The spectral feature map derived from the EEG signal via Variational Mode Decomposition (VMD) with a mode number (K) of 5, represents the frequency-based energy distribution. Subsequently, by integrating the Recursive Feature Elimination (RFE) algorithm with Support Vector Machine (SVM) algorithm employing cross-validation method, distinctive energy features in the 4-9Hz frequency band of coma patients compared to brain-death patients are identified. An accuracy of 99.59% and an F1-score of 99.61% for the SVM classifier demonstrate the high precision and reliability of the method. The application of specific machine learning algorithms provides robust theoretical support for the nuanced clinical interpretation of EEG signals across different levels of consciousness. This approach not only deepens scientific understanding of EEG signal variations associated with distinct consciousness levels but also establishes a solid foundation for future research aimed at quantifying EEG signal characteristics for the diagnosis and monitoring of brain diseases like epilepsy, Alzheimer's, and sleep disorders.