East China University of Science and Technology, Meilong Road 130, Shanghai 200237, China.
Saitama Institute of Technology, 1690 Fusaiji, Fukaya-shi, Saitama 369-0293, Japan ; Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.
Comput Math Methods Med. 2013;2013:618743. doi: 10.1155/2013/618743. Epub 2013 Dec 22.
To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states.
使用脑电图（EEG）方法来确定脑死亡，给出更明确的标准，对于降低风险和防止医疗误诊至关重要。本文提出了几种基于近似熵（ApEn）和样本熵（SampEn）的新的自适应可计算熵方法，用于监测患者的变化症状并确定脑死亡。所提出的方法通过引入移位时间窗口，是标准 ApEn 和 SampEn 的动态扩展。所开发的动态近似熵（DApEn）和动态样本熵（DSampEn）的主要优点是用于实时计算和实际使用。对 35 名患者（63 次记录）的分析结果表明，所提出的方法在评估大脑意识状态方面具有有效性和良好的性能。
