Computerized motion analysis of videotaped neonatal seizures of epileptic origin.
OBJECTIVE
The main objective of this research is the development of automated video processing and analysis procedures aimed at the recognition and characterization of the types of neonatal seizures. The long-term goal of this research is the integration of these computational procedures into the development of a stand-alone automated system that could be used as a supplement in the neonatal intensive care unit (NICU) to provide 24-h per day noninvasive monitoring of infants at risk for seizures.


METHODS
We developed and evaluated a variety of computational tools and procedures that may be used to carry out the three essential tasks involved in the development of a seizure recognition and characterization system: the extraction of quantitative motion information from video recordings of neonatal seizures in the form of motion-strength and motor-activity signals, the selection of quantitative features that convey some unique behavioral characteristics of neonatal seizures, and the training of artificial neural networks to distinguish neonatal seizures from random infant behaviors and to differentiate between myoclonic and focal clonic seizures.


RESULTS
The methods were tested on a set of 240 video recordings of 43 patients exhibiting myoclonic seizures (80 cases), focal clonic seizures (80 cases), and random infant movements (80 cases). The outcome of the experiments verified that optical- flow methods are promising computational tools for quantifying neonatal seizures from video recordings in the form of motion-strength signals. The experimental results also verified that the robust motion trackers developed in this study outperformed considerably the motion trackers based on predictive block matching in terms of both reliability and accuracy. The quantitative features selected from motion-strength and motor-activity signals constitute a satisfactory representation of neonatal seizures and random infant movements and seem to be complementary. Such features lead to trained neural networks that exhibit performance levels exceeding the initial goals of this study, the sensitivity goal being >or=80% and the specificity goal being >or=90%.


CONCLUSIONS
The outcome of this experimental study provides strong evidence that it is feasible to develop an automated system for the recognition and characterization of the types of neonatal seizures based on video recordings. This will be accomplished by enhancing the accuracy and improving the reliability of the computational tools and methods developed during the course of the study outlined here.
Karayiannis
Nicolaos B
NB
Department of Electrical and Computer Engineering, University of Houston, Houston, Texas 77204-4005, USA. Karayiannis@UH.EDU
Tao
Guozhi
G
Xiong
Yaohua
Y
Sami
Abdul
A
Varughese
Bindu
B
Frost
James D
JD
Wise
Merrill S
MS
Mizrahi
Eli M
EM
D001921:Brain / Q000503:physiopathology; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D020820:Dyskinesias / Q000175:diagnosis / Q000503:physiopathology; D004569:Electroencephalography / Q000379:methods / Q000706:statistics & numerical data; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology; D020936:Epilepsy, Benign Neonatal / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D018730:Infant Behavior / Q000502:physiology*; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal / Q000458:organization & administration; D009043:Motor Activity / Q000502:physiology; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D012640:Seizures / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted; D014743:Videotape Recording / Q000379:methods*