Methods Inf Med 2000; 39(02): 171-174
DOI: 10.1055/s-0038-1634270
Original Article
Schattauer GmbH

Injury Detection for Central Nervous System via EEG with Higher Order Crossing-based Methods

X. Kong
1   Department of Electrical Engineering, Northern Illinois University, De Kalb, IL, USA
,
T. Qiu
1   Department of Electrical Engineering, Northern Illinois University, De Kalb, IL, USA
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

Abstract:

Higher order crossing (HOC) is a powerful tool for time series analysis. Two HOC-based EEG analysis methods are developed for brain injury detection and quantification. The first method explores EEG spectrum characteristics via an estimate of the dominant frequency of a pre-processed EEG signal. The second method is based on the norm of the AHOC, an HOC obtained from the -filter prefiltered EEG signal. Both methods are shown to be effective in detecting hypoxic/asphyxic injuries as well as assessing the severity of the injury.

 
  • REFERENCES

  • 1 Viglione S, Martin W. Automatic analysis of the EEG for sleep staging. In: Automation of Clinical Electroencephalography. Kellaway P, Peterson I. Eds. New York: Raven Press; 1973: 269-85.
  • 2 Myers R, Stockard J, Saidman L. Monitoring of cerebral perfusion during anesthesia by time-compressed Fourier analysis of the electroencephalogram. Stroke 1977; 8: 331-7.
  • 3 Thatcher R, Walker R, Gerson I, Geisler F. EEG discriminant analysis of wild head trauma. Electroencephalography and Clinical Neurophysiology 1989; 73: 94-106.
  • 4 Gersch W. Spectral analysis of EEG’s by autoregressive decomposition of time series. Math Biosci 1970; 7: 205-22.
  • 5 Bodenstein G, Praetorius H. Feature extraction from electroencephalogram by adaptive segmentation. Proc IEEE 1977; 65: 642-52.
  • 6 Kong X, Brambrink A, Hanley DF, Thakor NV. Quantification of injury-related EEG signal changes using distance and information measures. IEEE Trans on Biomedical Engineering 1999; 46: 899-901.
  • 7 Goel V, Brambrink AM, Baykal A, Koehler RC, Hanley DF, Thalor NV. Dominant frequency analysis of EEG reveals brain’s response during injury and recovery. IEEE Trans on Biomedical Engineering 1996; 43: 1083-91.
  • 8 Kedem B. Time Series Analysis by Higher Order Crossings. New York: IEEE Press; 1994
  • 9 Kay SM, Sudhaker RA. A zero crossing-based spectrum analyzer: IEEE Trans on Acoustics. Speech, and Signal Processing 1986; 34: 96-104.
  • 10 Curtis SR, Shitz S, Oppenheim AL. Reconstruction of nonperiodic two dimensional signals from zero crossings. IEEE Trans on Acoustics, Speech, and Signal Processing 1987; 35: 890-3.
  • 11 Dickstein PA, Spelt JK, Sinclair AV. Application of a higher order crossing feature to non-destructive evaluation: a sample demonstration of sensitive to the condition of adhesive joints. Ultrasonics 1991; 29: 355-65.
  • 12 Kedem B. Higher-order crossings in time series identification. Technometrics 1987; 29: 193-204.