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Clinical Neuroengineering
Cardiac Arrest & Therapeutic Hypothermia
 Abstract
 
Cardiac Arrest (CA) is a leading cause of death in the United States (460,000/year) and the world (17 million/year). Despite the advances in resuscitation techniques, the survival rate is still very low for patients following out-of-hospital arrest. Poor functional recovery outcomes, such as coma, are prevalent among survivors, and neurological complications are the leading cause of mortality.
 
Hypothermia has been proven to be able to improve the neurological recovery outcomes from CA and early reperfusion in both animal models and preliminary human clinical trials. Yet, hypothermia is still underused as a therapeutic technique. Our group focuses on novel animal protocol design and new algorithm development for characterizing the degrees of neurological injuries to the brain and the effect of therapeutic hypothermia on the post-cardiac-arrest resuscitation.
Electroencephalogram (EEG)
Spikes (cortical and from other brain structures)
Evoked Potential (EP)
 

Globally Ischemic Cardiac Arrest Rat Model

Our globally ischemic CA experiments include models with different duration of CA (5-min, 7-min and 9-min). On the other hand, we also compare the different effects of conventional hypothermia and immediate hypothermia on post-resuscitation neurological recovery.
 
 

Novel Quantitative EEG Analysis

Recently, we have developed entropy-based methods to analyze EEG data. One approach measures the entropy using a probability distribution of signal amplitude in the time domain, while another approach, such as the Wavelet Entropy (WE), calculates measures how wide the power distributes in EEG frequency bands. WE is a good measure of spectral order/disorder but is less sensitive to amplitude change in time domain. A new measure, Information Quantity (IQ), improves upon the shortcomings of these two approaches. IQ can be interpreted as a unified entropy measure applicable to both time and frequency domains since it is based on time-frequency representation of a wavelet transform. IQ can be directly applied to quantitative EEG analysis of neurological injury and recovery. This measure is shown to be useful in titrating different grades of cardiac arrest injuries and hypothermic treatments.
 
a) Reduced bursting activity during induced cardiac arrest. b) Comparison between Information Quantity and Shannon Entropy. c) Comparison of EEG entropy between hypothermia and normothermia with IQ
 
Researchers
Xiaofeng Jia, MD, PhD
Youngesok Choi, PhD
 
Collaborators
Romer Geocadin, MD - Johns Hopkins School of Medicine (Neurology)
Daniel F. Hanley, MD - Johns Hopkins School of Medicine (Neurology)
Matthew Koenig, MD - Johns Hopkins School of Medicine (Neurology)
Carlos A. Prado, MD - Johns Hopkins School of Medicine (Neurology)
 
Funding
NIH R01
NIH R21
 
Publications
Jia X, Koenig MA, Venkatraman A, Thakor NV, Geocadin RG, Post-cardiac arrest temperature manipulation alters early EEG bursting in rats, Resuscitation, in press, 2008

Shin HC, Jia X, Nickl R, Geocadin RG, Thakor NV, A subband-based information measure of EEG during brain injury and recovery after cardiac arrest, IEEE Trans Biomed Eng, 55(8):1985-90, 2008

Kang X, Geocadin R, Thakor NV, Maybhate A, Multiscale entropy analysis of EEG for assessment of post-cardiac-arrest neurological recovery under hypothermia in rats, IEEE Trans Biomed Eng, in press, 2008

Geocadin RG, Koenig MA, Jia X, Stevens RD, Peberdy MA, Management of brain injury after resuscitation from cardiac arrest, Neurol Clin, 26(2):487-506, 2008

Jia X, Koenig MA, Shin HC, Zhen G, Pardo CA, Hanley DF, Thakor NV, Geocadin RG, Improving neurological outcomes post-cardiac arrest in a rat model: Immediate hypothermia and quantitative EEG monitoring, Resuscitation, 76(3):431-42, 2008
 
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