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One of the most important pieces of Information that an obstetrician can get, äs feedback in the attendance of childbirths, is the expected neonatal condition äs represented by the AP GAR score [1]. To evaluate the quality of labor monitoring, some guidance is needed during the course of labor. This cannot be achieved without such type of Information äs that which is usually called a controlled variable in control engineering. Although its essential significance äs to the later development of the baby's body and mind is somewhat controversial, a collaborative study reported by DRÄGE, KENNEDY, BERENDES, SCHWARZ and WEISS [3] has clearly demonstrated that the lower the points of the AP GAR score, the higher the incidence of neurological abnormalities at one year of age. It is also commonly known that the high mortality and morbidity rates are in the low AP GAR score group. Hence, in order to establish a mathematical formula for predicting the AP GAR scores at both one and five minutes after birth from several fetal parameters minimizing any possibility of personal, subjectively biased judgments, two different types of Computer prediction Systems have been developed. One is discriminant analysis of fetal variables to differentiate between the normal infants with an APGAR score of 8 or more and the depressed infants with a score of 7 or less. The other is partial regression analysis of fetal variables to construct a linear Curriculum vitae

Computer prediction of neonatal conditions by sampled data analyses of the fetal heart rate and the scalp blood during labor