I. Tsoulos - Academia.edu (original) (raw)
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Papers by I. Tsoulos
Lecture Notes in Computer Science, 2000
IEEE Intelligent Systems, 2006
Electronic fetal monitoring is an essential tool for fetal surveillance during labor. It is mainl... more Electronic fetal monitoring is an essential tool for fetal surveillance during labor. It is mainly based on the monitoring and evaluation of the fetal heart rate (FHR) signal, which has to be interpreted online. Evaluation and interpretation of FHR gives an indication of the fetal condition. A lot of research efforts have been done towards the development of automatic and
2013 21st Iranian Conference on Electrical Engineering (ICEE), 2013
Third IEEE Conference …
Abstract⎯Electronic fetal monitoring is an essential tool for fetal surveillance during labour. I... more Abstract⎯Electronic fetal monitoring is an essential tool for fetal surveillance during labour. It is mainly based on the monitoring and evaluation of the Fetal Heart Rate, (FHR) which is a biosignal that has to be interpreted on line. Evaluation and interpretation of FHR gives an indication ...
Fetal heart rate (FHR) variations reflect the level of oxygenation and blood pressure of the fetu... more Fetal heart rate (FHR) variations reflect the level of oxygenation and blood pressure of the fetus. Electronic Fetal Monitoring (EFM), the
continuous monitoring of the FHR, was introduced into clinical practice in the late 1960s and since then it has been considered as an indispensable
tool for fetal surveillance. However, EFM evaluation and its merit is still an open field of controversy, mainly because it is not consistently
reproducible and effective. In this work, we present a novel method based on grammatical evolution to discriminate acidemic from normal fetuses,
utilizing features extracted from the FHR signal during the minutes immediately preceding delivery. The proposed method identifies linear and
nonlinear correlations among the originally extracted features and creates/constructs a set of new ones, which, in turn, feed a nonlinear classifier.
The classifier, which also uses a hybrid method for training, along with the constructed features was tested using a set of real data achieving an
overall performance of 90% (specificity = sensitivity = 90%).
Lecture Notes in Computer Science, 2000
IEEE Intelligent Systems, 2006
Electronic fetal monitoring is an essential tool for fetal surveillance during labor. It is mainl... more Electronic fetal monitoring is an essential tool for fetal surveillance during labor. It is mainly based on the monitoring and evaluation of the fetal heart rate (FHR) signal, which has to be interpreted online. Evaluation and interpretation of FHR gives an indication of the fetal condition. A lot of research efforts have been done towards the development of automatic and
2013 21st Iranian Conference on Electrical Engineering (ICEE), 2013
Third IEEE Conference …
Abstract⎯Electronic fetal monitoring is an essential tool for fetal surveillance during labour. I... more Abstract⎯Electronic fetal monitoring is an essential tool for fetal surveillance during labour. It is mainly based on the monitoring and evaluation of the Fetal Heart Rate, (FHR) which is a biosignal that has to be interpreted on line. Evaluation and interpretation of FHR gives an indication ...
Fetal heart rate (FHR) variations reflect the level of oxygenation and blood pressure of the fetu... more Fetal heart rate (FHR) variations reflect the level of oxygenation and blood pressure of the fetus. Electronic Fetal Monitoring (EFM), the
continuous monitoring of the FHR, was introduced into clinical practice in the late 1960s and since then it has been considered as an indispensable
tool for fetal surveillance. However, EFM evaluation and its merit is still an open field of controversy, mainly because it is not consistently
reproducible and effective. In this work, we present a novel method based on grammatical evolution to discriminate acidemic from normal fetuses,
utilizing features extracted from the FHR signal during the minutes immediately preceding delivery. The proposed method identifies linear and
nonlinear correlations among the originally extracted features and creates/constructs a set of new ones, which, in turn, feed a nonlinear classifier.
The classifier, which also uses a hybrid method for training, along with the constructed features was tested using a set of real data achieving an
overall performance of 90% (specificity = sensitivity = 90%).