Whole-brain CT Perfusion at Admission and During Delayed Time-window Detects the Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage

Objective To evaluate the utility of computed tomography perfusion(CTP)both at admission and during delayed cerebral ischemia time-window(DCITW)in the detection of delayed cerebral ischemia(DCI)and the change in CTP parameters from admission to DCITW following aneurysmal subarachnoid hemorrhage.Methods Eighty patients underwent CTP at admission and during DCITW.The mean and extreme values of all CTP parameters at admission and during DCITW were compared between the DCI group and non-DCI group,and comparisons were also made between admission and DCITW within each group.The qualitative color-coded perfusion maps were recorded.Finally,the relationship between CTP parameters and DCI was assessed by receiver operating characteristic(ROC)analyses.Results With the exception of cerebral blood volume(P=0.295,admission;P=0.682,DCITW),there were significant differences in the mean quantitative CTP parameters between DCI and non-DCI patients both at admission and during DCITW.In the DCI group,the extreme parameters were significantly different between admission and DCITW.The DCI group also showed a deteriorative trend in the qualitative color-coded perfusion maps.For the detection of DCI,mean transit time to the center of the impulse response function(Tmax)at admission and mean time to start(TTS)during DCITW had the largest area under curve(AUC),0.698 and 0.789,respectively.Conclusion Whole-brain CTP can predict the occurrence of DCI at admission and diagnose DCI during DCITW.The extreme quantitative parameters and qualitative color-coded perfusion maps can better reflect the perfusion changes of patients with DCI from admission to DCITW.

LMS ADAPTIVE TIME DELAY ESTIMATION WITH TWO WINDOWS

An LMS adaptive time delay estimation method with two windows is presented. This method can reduce the superfluous calculation greatly when the time of correlation is long. It is suitable for the time delay estimation of white band-limited random signals. The feasibility and the performances of this method are also studied.

Time Delay Estimation Based on Entropy Estimation

This paper presents a novel time delay estimation （TDE） method using the concept of entropy. The relative delay is estimated by minimizing the estimated joint entropy of multiple sensor output signals. When estimating the entropy, the information about the prior distribution of the source signal is not required. Instead, the Parzen window estimator is employed to estimate the density function of the source signal from multiple sensor output signals. Meanwhile, based on the Parzen window estimator, the Renyi＇s quadratic entropy （RQE） is incorporated to effectively and efficiently estimate the high-dimensional joint entropy of the multichannel outputs. Furthermore, a modified form of the joint entropy for embedding information about reverberation （multipath reflections） for speech signals is introduced to enhance the estimator＇s robustness against reverberation.

Selection of Optimal Embedding Parameters Applied to Short and Noisy Time Series from Rossler System

Throughout scientific research, the state space reconstruction that embeds a non-linear time series is the first and necessary step for characterizing and predicting the behavior of a complex system. This requires to choose appropriate values of time delay T and embedding dimension dE. Three methods are applied and discussed on nonlinear time series provided by the Rössler attractor equations set: Cao’s method, the C-C method developed by Kim et al. and the C-C-1 method developed by Cai et al. A way to fix a parameter necessary to implement the last method is given. Focus has been put on small size and/or noisy time series. The reconstruction quality is measured by using a criterion based on the transformation smoothness.