Automated Analysis of Cardiotocograms

Orjinal Çalışma ;

SisPorto 2.0: a program for automated analysis of cardiotocograms

Özeti ;

To describe the latest version of SisPorto, a program for automated analysis of cardiotocograms that closely follows the FIGO guidelines, analyses ante- and intrapartum tracings, performs no signal reduction, and has the possibility of simultaneously recording twins.A detailed description of the program's processing algorithms and operation is provided, as well as the main results of the studies performed to-date with this system.Considering both current and previous versions of the program, SisPorto has been tested in over 6000 pregnancies. The system's FHR baseline was compared with an average of three experts' estimates, and the difference was under 8 bpm in all cases. A fair to good agreement was found with experts' identification of accelerations, decelerations, contractions, and normal/reduced variability (proportions of agreement 0.64-0.89). In a preliminary validity study (n = 85), a sensitivity of 100% and a specificity of 99% were obtained in prediction of poor neonatal outcome. The system is currently undergoing an international multicentre validation study.Although still at the research level, a considerable experience has now been gathered with this system. Promising results have been achieved in studies comparing SisPorto with experts' analysis and in those evaluating the validity of the system.

Çalışmanın verilerine The UCI Machine Learning Repository den Cardiotocography Data Set  başlığından ulaşabilirsiniz. Cardiotocography Anne karnındaki bebeğin kalp sağlığının belirlenmesi için kullanınal bir teknoloji. Sisporto ise Omniview-SisPorto adıyla ticarileşmiş bir proje artık.

Verilerin detayına bakacak olursak;

2126 fetal cardiotocograms (CTGs) were automatically processed and the respective diagnostic features measured. The CTGs were also classified by three expert obstetricians and a consensus classification label assigned to each of them. Classification was both with respect to a morphologic pattern (A, B, C. ...) and to a fetal state (N, S, P). Therefore the dataset can be used either for 10-class or 3-class experiments.

Attribute Information:

LB - FHR baseline (beats per minute)
AC - # of accelerations per second
FM - # of fetal movements per second
UC - # of uterine contractions per second
DL - # of light decelerations per second
DS - # of severe decelerations per second
DP - # of prolongued decelerations per second
ASTV - percentage of time with abnormal short term variability
MSTV - mean value of short term variability
ALTV - percentage of time with abnormal long term variability
MLTV - mean value of long term variability
Width - width of FHR histogram
Min - minimum of FHR histogram
Max - Maximum of FHR histogram
Nmax - # of histogram peaks
Nzeros - # of histogram zeros
Mode - histogram mode
Mean - histogram mean
Median - histogram median
Variance - histogram variance
Tendency - histogram tendency
CLASS - FHR pattern class code (1 to 10)
NSP - fetal state class code (N=normal; S=suspect; P=pathologic)

 Orjinal verinin 2 çeşit çıktısı var. İlkinde 10 çeşit FHR tipinden hangisi olduğu, ikincisinde ise üç çeşit Fetal State için (N=normal; S=suspect; P=pathologic) bir sınıflandırma var. Biz Örneğimiz için ikincisini kullandık.
Bütün YSA işlemleri FannTool ile yapıldı. Bir Önceki örneğimizdeki gibi program yazmadık sonuçalrı Excell Dosyasındaki "Full Sonuç-Eğitim" ve "Full Sonuç-Test" sayfalarında görebilirsiniz

Eğitim Verileri için Başarı Oranı :  % 98,72  (  1488 Örnekden  1469 Doğru )
Test Verileri için  Başarı Oranı   :  % 94.2  ( 638 Örnekden 601 Doğru  )
Dosyalar İndirmek İçin :  CTG.zip