title: Computer interpreted fetal electroencephalogram: sharp wave detection and classification of infants for one year neurological outcome.
abstract: The presence of visually discernible sharp waves (SWs) in the fetal electroencephalogram (FEEG) has been found to be associated with abnormal neurological infant outcome, but no method of programmed SW detection for FEEG was available. In order to develop an algorithm for SW detection, the first and second derivatives for visually identified SWs and non-SWs were examined and five random variables chosen for discriminant function analysis (DFA). The resulting equation, incorporated into program logic along with logic for artifact rejection, produced classifications from 85% to 89% consistent with visual identifications, suggesting that the number of SWs/epoch (NSW) corresponds with visually identified SWs. In addition, in 61 cases using a threshold for NSW derived by DFA, computer recognized SWs were found to be significantly related to the overall visual interpretation of the tracings (P less than 0.005). Finally, NSW alone produced correct classification of 65.5% of infants for 1 year neurological outcome. The overall consistency was increased to as high as 80% using additional FEEG and neonatal data. These findings imply that some forms of brain damage are present before birth and can be detected during labor using FEEG.
mesh_terms: D001927:Brain Diseases / Q000175:diagnosis; D003201:Computers; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D005315:Fetal Diseases / Q000175:diagnosis*; D005500:Follow-Up Studies; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008991:Monitoring, Physiologic / Q000379:methods*; D009460:Neurologic Examination; D010363:Pattern Recognition, Automated; D011247:Pregnancy