DocumentCode :
899639
Title :
Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines
Author :
Georgoulas, G. ; Stylios, D. ; Groumpos, P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Patras, Rion
Volume :
53
Issue :
5
fYear :
2006
fDate :
5/1/2006 12:00:00 AM
Firstpage :
875
Lastpage :
884
Abstract :
Cardiotocography is the main method used for fetal assessment in everyday clinical practice for the last 30 years. Many attempts have been made to increase the effectiveness of the evaluation of cardiotocographic recordings and minimize the variations of their interpretation utilizing technological advances. This research work proposes and focuses on an advanced method able to identify fetuses compromised and suspicious of developing metabolic acidosis. The core of the proposed method is the introduction of a support vector machine to "foresee" undesirable and risky situations for the fetus, based on features extracted from the fetal heart rate signal at the time and frequency domains along with some morphological features. This method has been tested successfully on a data set of intrapartum recordings, achieving better and balanced overall performance compared to other classification methods, constituting,therefore, a promising new automatic methodology for the prediction of metabolicacidosis
Keywords :
cardiology; feature extraction; medical signal processing; obstetrics; support vector machines; time-frequency analysis; cardiotocography; feature extraction; fetal heart rate signal classification; intrapartum recording; metabolic acidosis; newborns; support vector machines; time-frequency domain; Cardiography; Data mining; Feature extraction; Fetal heart rate; Fetus; Frequency domain analysis; Pattern classification; Pediatrics; Support vector machine classification; Support vector machines; Feature extraction; fetal heart rate (FHR); intrapartum monitoring; metabolic acidosis; support vector machines (SVMs); Acidosis; Algorithms; Artificial Intelligence; Cardiotocography; Cluster Analysis; Diagnosis, Computer-Assisted; Heart Rate, Fetal; Humans; Infant, Newborn; Pattern Recognition, Automated; Reproducibility of Results; Risk Assessment; Risk Factors; Sensitivity and Specificity; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2006.872814
Filename :
1621139
Link To Document :
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