Analyses of Transient and Time-Varying Evoked Potentials for Detection of Brain Injury

  • Nitish V. Thakor
  • Xuan Kong

Abstract

In clinical situations, such as high-risk surgical or neurological critical care, evoked potential (EP) monitoring may help identify incidence of brain injury. Experimental models of brain injury, such as cerebral hypoxia or ischemia, have been created to help understand the cerebral physiology and to develop algorithms to detect such events. The problem here is to identify transient or time-varying changes in EP signals during continuous monitoring of brain in noisy environments. Several signal processing techniques have been developed to identify injury-related changes in EP signals: adaptive filtering, adaptive Fourier series modeling, adaptive delay estimation and adaptive coherence estimation. The adaptive filtering algorithm analyzes time-varying changes in EP signals while improving the signal-to-noise ratio. Application of this algorithm helped capture, in controlled experimental studies and intraoperatively, transient events such as clipping of cerebral artery and response to anesthetics, respectively. The adaptive Fourier series modeling algorithm constructs a model of normal EP signal from which injury-related changes can be inferred. This algorithm showed that neurological injury, such as caused by cerebral hypoxia, results in frequency dispersion; this observation may be used as an early indicator of brain injury in critical care settings. The adaptive coherence estimation algorithm is employed to determine if the injury-related response causes nonlinear transformation of the measured signals. A linearity index, derived from the coherence estimates, helps identify at an early stage the irreversible course of brain injury. In conclusion, several advanced signal processing algorithms have been developed to demonstrate that transient and time-varying changes in neurological signals can be used to monitor the brain’s response to injury in critical care situations.

Keywords

Fatigue Ischemia Coherence Expense Neurol 

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Nitish V. Thakor
    • 1
  • Xuan Kong
    • 2
  1. 1.Biomedical Engineering DepartmentJohns Hopkins School of MedicineBaltimoreUSA
  2. 2.Electrical Engineering DepartmentNorthern Illinois UniversityDeKalbUSA

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