Analyzing patients' EEG energy for brain death determination based on Dynamic 2T-EMD

Yao Miao; Dongsheng Wang; Gaochao Cui; Li Zhu; Jianting Cao

doi:10.24297/ijct.v16i1.5934

Authors

Yao Miao Saitama Institute of Technology, Fusaiji 1690, Fukaya, Saitama
Dongsheng Wang Saitama Institute of Technology, Fusaiji 1690, Fukaya, Saitama
Gaochao Cui Saitama Institute of Technology, Fusaiji 1690, Fukaya, Saitama
Li Zhu School of Information Science and Engineering, Xiamen University, Xiamen, Fujian
Jianting Cao Saitama Institute of Technology, Fusaiji 1690, Fukaya, Saitama

DOI:

https://doi.org/10.24297/ijct.v16i1.5934

Keywords:

EEG energy analysis; Dynamic 2T-EMD; Brain death determination

Abstract

EEG (electroencephalography) energy is an important evaluation indicator in brain death determination based on EEG analysis. In related works, the static EEG energy value can be discovered using EMD (empirical mode decomposition), MEMD (multivariate empirical mode decomposition) and 2T-EMD (turning tangent empirical mode decomposition) for EEG-based coma and quasi-brain-death analysis. However such methods are not time-varying and feasible. In this paper, we firstly propose the Dynamic 2T-EMD algorithm to evaluate the dynamic patients' EEG energy variation by the means of time window and time step method. With the time window sliding along the time axis in a time step, EEG energy of corresponding time step is computed and stored. The proposed algorithm is applied to analyze 19 cases of coma patients' EEG and 17 cases of quasi-brain-death patients' EEG. Two typical patients in coma and quasi-brain-death state and one special case who was from coma to quasi-brain-death have been taken as examples to give the algorithm performance. Results show that EEG energy in coma state are obviously higher than that in quasi-brain-death state, and even present the EEG energy change trend of every case, which can prevent loss of information and wrong analysis results caused by noise interference and provide scientific basis for doctors to evaluate patients' consciousness levels in brain death determination. The proposed algorithm will be very helpful to develop the real time brain death diagnostic system.

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