Abstract:
: Clinically cardiotocography is a technique which is used to monitor and evaluate the level of fetal dis tress. Even though, CTG is the most widely used device to monitor determine the fetus health, existence of high false
positive result from the visual interpretation has a signifcant contribution to unnecessary surgical delivery or delayed
intervention.
Objective: In the current study an innovative computer aided fetal distress diagnosing model is developed by using
time frequency representation of FHR signal using generalized Morse wavelet and the concept of transfer learning of
pre-trained ResNet 50 deep neural network model.
Method: From the CTG data that is obtained from the only open access CTU-UHB data base only FHR signal is
extracted and preprocessed to remove noises and spikes. After preprocessing the time frequency information of FHR
signal is extracted by using generalized Morse wavelet and fed to a pre-trained ResNet 50 model which is fne tuned
and confgured according to the dataset.
Main outcome measures: Sensitivity (Se), specifcity (Sp) and accuracy (Acc) of the model adopted from binary
confusion matrix is used as outcome measures.
Result: After successfully training the model, a comprehensive experimentation of testing is conducted for FHR data
for which a recording is made during early stage of labor and last stage of labor. Thus, a promising classifcation result
which is accuracy of 98.7%, sensitivity of 97.0% and specifcity 100% are achieved for FHR signal of 1st stage of labor.
For FHR recorded in last stage of labor, accuracy of 96.1%, sensitivity of 94.1% and specifcity 97.7% are achieved
