Neonatal seizures are often the first, and, in some situations, the only clinical sign of central nervous system dysfunction in the newborn. Despite the importance of early detection and characterization of neonatal seizures with regard to diagnosis and management of underlying neurological problems, most neonatal intensive care units and nurseries have limited resources for seizure monitoring, detection and characterization. The research outlined in this proposal is the first attempt ever to utilize recent advances in video and computer technology toward the development of automated video processing and analysis procedures that can facilitate the characterization and recognition of neonatal seizures. These procedures will rely on quantitative information regarding the behavioral characteristics of neonatal seizures, which will be extracted from videotaped neonatal seizures in the form of temporal motion strength and motor activity signals. The proposed research is expected to produce novel computational tools for extracting quantitative information from image sequences, which may be utilized to support diagnosis and extend human analysis. The automated video processing and analysis procedures developed in this project will be evaluated and tested on an existing library of videotaped clinical events, which include neonatal seizures and normal or abnormal infant behaviors not due to seizures. The long-term goal of this research is the integration of the proposed video processing and analysis procedures into the development of a stand-alone automated seizure detection and characterization system that could be used as a supplement in the neonatal intensive care unit to: 1) provide 24-hour a day noninvasive monitoring of infants at risk for seizures, and 2) facilitate the analysis and characterization of videotaped neonatal seizures by physicians during retrospective review.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB000183-03
Application #
6764114
Study Section
Diagnostic Imaging Study Section (DMG)
Program Officer
Peng, Grace
Project Start
2002-07-15
Project End
2007-06-30
Budget Start
2004-07-01
Budget End
2007-06-30
Support Year
3
Fiscal Year
2004
Total Cost
$198,500
Indirect Cost
Name
University of Houston
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
036837920
City
Houston
State
TX
Country
United States
Zip Code
77204
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Karayiannis, Nicolaos B; Xiong, Yaohua; Frost Jr, James D et al. (2006) Automated detection of videotaped neonatal seizures based on motion tracking methods. J Clin Neurophysiol 23:521-31
Karayiannis, Nicolaos B; Xiong, Yaohua; Tao, Guozhi et al. (2006) Automated detection of videotaped neonatal seizures of epileptic origin. Epilepsia 47:966-80
Karayiannis, Nicolaos B; Xiong, Yaohua (2006) Training reformulated radial basis function neural networks capable of identifying uncertainty in data classification. IEEE Trans Neural Netw 17:1222-34
Karayiannis, Nicolaos B; Tao, Guozhi; Xiong, Yaohua et al. (2005) Computerized motion analysis of videotaped neonatal seizures of epileptic origin. Epilepsia 46:901-17
Karayiannis, Nicolaos B; Sami, Abdul; Frost Jr, James D et al. (2005) Automated extraction of temporal motor activity signals from video recordings of neonatal seizures based on adaptive block matching. IEEE Trans Biomed Eng 52:676-86
Karayiannis, Nicolaos B; Xiong, Yaohua; Frost Jr, James D et al. (2005) Quantifying motion in video recordings of neonatal seizures by robust motion trackers based on block motion models. IEEE Trans Biomed Eng 52:1065-77
Karayiannis, Nicolaos B; Xiong, Yaohua; Frost Jr, James D et al. (2005) Improving the accuracy and reliability of motion tracking methods used for extracting temporal motor activity signals from video recordings of neonatal seizures. IEEE Trans Biomed Eng 52:747-9
Karayiannis, Nicolaos B; Varughese, Bindu; Tao, Guozhi et al. (2005) Quantifying motion in video recordings of neonatal seizures by regularized optical flow methods. IEEE Trans Image Process 14:890-903