Abstract
Prediction of preterm birth is one of the significant perinatal hurdles for the prevention of preterm birth. The uterine Electromyogram (Uterine EMG), obtained from the abdominal surface is analyzed for the prediction or preterm labor. Many linear and non-linear features and classifiers have been analyzed in different researches. In this paper two neural network classifiers were applied to the Bi-spectrum feature obtained from the Uterine EMG signal. The Bi-spectrum analysis was done after preprocessing the signal. Three pre-processing methods were tried to improve the performance. The best classification accuracy of 99.89% was obtained with Elman neural network classifier when pre-processed with three level wavelet (db4) decomposition. The sensitivity and specificity were found to be 100% and 99.77% respectively.
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Kamalraj Subramaniam, Shaniba Asmi, P., Iqbal, N.V. (2020). A Bi-spectrum Analysis of Uterine Electromyogram Signal Towards the Prediction of Preterm Birth. In: Balas, V., Kumar, R., Srivastava, R. (eds) Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-030-32644-9_8
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