Sahana Das

Work place: Narula Institute of Technology, Department of Computer Application, Kolkata, 700109, India



Research Interests: Medical Informatics, Artificial Intelligence, Computational Learning Theory, Pattern Recognition, Image Compression, Image Manipulation, Image Processing, Medical Image Computing


Sahana Das, received her B.Sc. from University of Swaziland, Southern Africa and MCA from Indira Gandhi National Open University in 1995 and 2004 respectively. Currently she is an Assistant Professor in the Department of Computer Application, Narula Institute of Technology, Kolkata. She is a PhD research fellow in West Bengal State University, Barasat. Her areas of research include Pattern Recognition, Medical Image Processing, Machine Learning and Artificial Intelligence.

Author Articles
Establishment of Automated Technique of FHR Baseline and Variability Detection Using CTG: Statistical Comparison with Expert’s Analysis

By Sahana Das Kaushik Roy Chanchal K. Saha

DOI:, Pub. Date: 8 Jan. 2019

The most widely accepted method of monitoring the fetal heart rate and uterine activity of the mother is using Cardiotocograph (CTG). It simultaneously captures these two signals and correlate them to find the status of the fetus. This method is preferred by obstetricians since it is non-invasive as well as cost-effective. Though used widely, the specificity and predictive precision has not been undisputable. The main reason behind this is due to the contradiction in clinicians opinions. The two main components of CTG are Baseline and Variability which provide a thorough idea about the state of the fetal-health when CTG signals are inspected visually. These parameters are indicative of the oxygen saturation level in the fetal blood. Automated detection and analysis of these parameters is necessary for early and accurate detection of hypoxia, thus avoiding further compromise. Results of the proposed algorithm were compared with the visual assessment performed by three clinicians in this field using various statistical techniques like Confidence Interval (CI), paired sample t-test and Bland-Altman plot. The agreement between the proposed method and the clinicians’ evaluation is strong.

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