PERFORMANCE ANALYSIS OF GRANULAR COMPUTING MODEL IN SOFT COMPUTING PARADIGM FOR MONITORING OF FETAL ECHOCARDIOGRAPHY

The monitoring of fetal heart being essential in the second trimester of the prenatal periods. The abnormalities in the child heart rate has to be identified in the early stages, so as to take essential remedies for the babies in the womb, or would enable the physician to be ready for he complication on the delivery and the further treatment after the baby is received. The traditional methodologies being ineffective in detecting the abnormalities leading to fatalities, paves way for the granular computing based fuzzy set, that requires only a limited set of data for training, and helps in the eluding of the unwanted data set that are far beyond the optimal. Further the methods performance is analyzed to evident the improvement in the fetal heart rate detection in terms of prediction accuracy and the detection accuracy.

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Computer-assisted analysis of normal second trimester fetal heart rate patterns

Journal of Perinatal Medicine ◽

10.1515/jpme.1991.19.1-2.53 ◽

1991 ◽

Vol 19 (1-2) ◽

pp. 53-59 ◽

Cited By ~ 32

Author(s):

Lucia S. M. Ribbert ◽

Vaclav Fidler ◽

Gerard H. A. Visser

Keyword(s):

Heart Rate ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Computer Assisted ◽

Second Trimester ◽

Assisted Analysis

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Longitudinal measurements of fetal heart rate (FHR) monitoring in second trimester

Early Human Development ◽

10.1016/0378-3782(92)90161-9 ◽

1992 ◽

Vol 29 (1-3) ◽

pp. 251-257

Author(s):

Sato Ikuo ◽

Izumi Akio ◽

Tamada Taro

Keyword(s):

Heart Rate ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Second Trimester ◽

Longitudinal Measurements

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The use of fetal echocardiography for predicting intrapartum fetal heart rate patterns in the post-term pregnancy

Ultrasound in Obstetrics and Gynecology ◽

10.1046/j.1469-0705.1991.01060395.x ◽

1991 ◽

Vol 1 (6) ◽

pp. 395-400 ◽

Cited By ~ 1

Author(s):

J. Horenstein ◽

G. R. DeVore ◽

L. D. Platt ◽

B. Siassi ◽

C. Walla ◽

...

Keyword(s):

Heart Rate ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Fetal Echocardiography ◽

Term Pregnancy ◽

Post Term Pregnancy

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Decision Tree Method Using for Fetal State Classification from Cardiotography Data

Journal of Advanced Engineering and Computation ◽

10.25073/jaec.202041.273 ◽

2020 ◽

Vol 4 (1) ◽

pp. 64

Author(s):

Md Zannatul Arif ◽

Rahate Ahmed ◽

Umma Habiba Sadia ◽

Mst Shanta Islam Tultul ◽

Rocky Chakma

Keyword(s):

Heart Rate ◽

Decision Tree ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Classification Tree ◽

Training Data ◽

Single Node ◽

Data Set ◽

Decision Tree Method ◽

Tree Method

The motive of the investigation is analyzing the categorization of fetal state code from the Cardiographic data set based on decision tree method. Cardiotocography is one of the important tools for monitoring heart rate, and this technique is widely used worldwide. Cardiotocography is applied for diagnosing pregnancy and checking fetal heart rate state condition until before delivery. This classification is necessary to predict fetal heart rate situation which is belonging. In this paper, we are using three input attributes of training data set quoted by LB, AC, and FM to categorize as normal, suspect or pathological where NSPF variable is used as a response variable. After drawing necessary analysis into three variables we get the 19 nodes of classification tree and also we have measured every single node according to statistic, criterion, weights, and values. The Cardiotocography Dataset applied in this study is received from UCI Machine Learning Repository. The dataset contains 2126 observation instances with 22 attributes. In this experiment, the highest accuracy is 98.7%. Overall, the experimental results proved the viability of Classification and Regression Trees and its potential for further predictions.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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