Monitoring of global cerebral ischemia using wavelet entropy rate of change

In this paper, the subband wavelet entropy (SWE) and its time difference are proposed as two quantitative measures for analyzing and segmenting the electroencephalographic (EEG) signals. SWE for EEG subbands, namely Delta, Theta, Alpha, Beta, and Gamma, is calculated and segmented using wavelet analysis. In addition, a time difference entropy measure was calculated because it does not require a baseline and equals to zero in all clinical bands as the initial condition. Visual and quantitative results were obtained from 11 rodents that were subjected to 3, 5, and 7 min of global ischemic brain injury by asphyxic cardiac arrest. We found that the time difference of SWE is capable of amplifying the variations between clinical bands during the various stages of the recovery process and may serve as a novel analytical approach to grade and classify brain rhythms during global ischemic brain injury and recovery.

[1]  Nitish V. Thakor,et al.  Wavelet Entropy for Subband Segmentation of EEG During Injury and Recovery , 2003, Annals of Biomedical Engineering.

[2]  N. Thakor,et al.  Dominant frequency analysis of EEG reveals brain's response during injury and recovery , 1996, IEEE Transactions on Biomedical Engineering.

[3]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[4]  Fritz Sterz,et al.  Mild resuscitative hypothermia and outcome after cardiopulmonary resuscitation. , 1996 .

[5]  N. Thakor,et al.  A novel quantitative EEG injury measure of global cerebral ischemia , 2000, Clinical Neurophysiology.

[6]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[7]  R. Thatcher,et al.  EEG discriminant analyses of mild head trauma. , 1989, Electroencephalography and clinical neurophysiology.

[8]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[9]  B. P. Lathi,et al.  Modern Digital and Analog Communication Systems , 1983 .

[10]  A. Medl,et al.  Time Frequency and Wavelets in Biomedical Signal Processing , 1998, IEEE Engineering in Medicine and Biology Magazine.

[11]  N V Thakor,et al.  Quantitative EEG during Early Recovery from Hypoxic-Ischemic Injury in Immature Piglets: Burst Occurrence and Duration , 1999, Clinical EEG.

[12]  R. Gray.  Entropy and Information Theory , 1990, Springer New York.

[13]  Erik W. Jensen,et al.  EEG complexity as a measure of depth of anesthesia for patients , 2001, IEEE Trans. Biomed. Eng..

[14]  C M O'Connor,et al.  Cardiopulmonary resuscitation: historical perspective to recent investigations. , 1999, American heart journal.

[15]  P L Smith,et al.  EEG changes during cardiopulmonary bypass surgery and postoperative neuropsychological deficit: the effect of bubble and membrane oxygenators. , 1997, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[16]  R. Neumar,et al.  Outcome Model of Asphyxial Cardiac Arrest in Rats , 1995, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.