Key result
A deep learning model using generalized Morse wavelet and ResNet 50 achieved 98.7% accuracy, 97.0% sensitivity, and 100% specificity for detecting fetal distress from first-stage labor cardiotocogram signals.
Why the study?
Visual interpretation of cardiotocography has a high false-positive rate, contributing to unnecessary surgical delivery or delayed intervention.
Does a deep learning model using Morse wavelet time-frequency representation of FHR signals accurately detect fetal distress?
Does a deep learning model using Morse wavelet time-frequency representation of FHR signals accurately detect fetal distress?
A deep learning model using generalized Morse wavelet time-frequency representation of fetal heart rate signals demonstrated high accuracy, sensitivity, and specificity for detecting fetal distress during labor.
Answers are grounded in this paper and cite their sources.
New CTG model may reduce false-positive fetal distress calls; leaves open whether it improves outcomes in prospective use.
BACKGROUND: Clinically cardiotocography is a technique which is used to monitor and evaluate the level of fetal distress. 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 significant 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 fine tuned and configured according to the dataset. MAIN OUTCOME MEASURES: Sensitivity (Se), specificity (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 classification result which is accuracy of 98.7%, sensitivity of 97.0% and specificity 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 specificity 97.7% are achieved. CONCLUSION: The developed model can be used as a decision-making aid system for obstetrician and gynecologist.
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Daydulo et al. (2022) studied Fetal distress (n=552). Deep learning model (ResNet 50) with Morse wavelet time-frequency representation was evaluated on Accuracy, sensitivity, and specificity for fetal distress detection (1st stage of labor). A deep learning model using generalized Morse wavelet and ResNet 50 achieved 98.7% accuracy, 97.0% sensitivity, and 100% specificity for detecting fetal distress from first-stage labor cardiotocogram signals.
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