Brain Death Determination Aid System Using Automatic D-2TEMD

Electroencephalography (EEG) analysis systems play a crucial role in brain death determination, where noise suppression and robustness are of utmost importance. In this study, we address these concerns by proposing improvements to the dynamic transitive tangent empirical mode decomposition (D-2TEMD) algorithm that allow automatic selection of the necessary intrinsic mode functions (IMFs). We evaluate EEG energy as an essential indicator for brain death determination by calculating the area of the IMF spectrogram. In addition, we use dynamic approximate entropy (D-ApEn) to assess the complexity of the EEG signal after applying automatic D-2TEMD noise reduction. To demonstrate the effectiveness of our approach, we develop an automated brain death determination system that analyzes EEG data from coma and brain death cases. Through the analysis of system examples, we establish the validity and effectiveness of the proposed system.