Title :
On the application of model based distance metrics of signals for detection of brain injury
Author :
Paul, J.S. ; Tong, S. ; Sherman, D. ; Bezerianos, A. ; Thakor, N.V.
Author_Institution :
Dept. of Biomed. Eng., Johns Hopkins Univ. Sch. of Med., Baltimore, MD, USA
fDate :
6/23/1905 12:00:00 AM
Abstract :
In the basic and clinical research on brain´s response to injury, electrical signals from the brain, namely EEG, is useful in providing an immediate signaling of the dysfunction. However, EEG signals have proven to be difficult to analyze and interpret due it its complex signal characteristic. There is a critical need for developing robust and reliable measures that can be correlated with injury as well as survival. In this paper, we address a unique problem of characterizing quantitatively the electrical measures of brain injury for analysis of brain activity in animal and human subjects. The key objective is to model EEG spectra and its features so that signaling changes due to injury can be discovered. We do so with the method of autoregressive modeling and dominant frequency analysis. The trends in the electrical signaling following injury and following resuscitation are modeled using the cepstral distance derived from the AR model
Keywords :
brain models; electroencephalography; frequency-domain analysis; medical signal detection; medical signal processing; spectral analysis; EEG spectra modeling; animal subjects; autoregressive modeling method; brain injury detection; brain injury electrical measures; cardiac arrest; cepstral distance; dysfunction signaling; electrodiagnostics; human subjects; resuscitation; Animals; Brain injuries; Brain modeling; Cepstral analysis; Electric variables measurement; Electroencephalography; Frequency; Humans; Robustness; Signal analysis;
Conference_Titel :
Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on
Print_ISBN :
0-7803-7011-2
DOI :
10.1109/SSP.2001.955271
