Classification of Fractal features of Uterine EMG Signal for the Prediction of Preterm Birth
Shaniba Asmi P, Kamalraj Subramaniam and Nisheena V. Iqbal

Department of Electronics and Communication Engineering, Karpagam university, Coimbatore  India.

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Abstract: Early diagnosing is one of the important perinatal challenges for the prevention of preterm birth. The electrohysterogram (EHG) or uterine electromyogram (Uterine EMG), collected from the abdominal surface   is considered as a biomarker for the prediction or preterm labor. Several features and classifiers have been analyzed in different studies. Four classifiers were applied to two fractal features , say, Higuchi Fractal dimension(HFD) and Detrended Fluctuation Analysis (DFA), after filtering with fourth order band pass filter. The best classification accuracy (95.7989%) was obtained with Elman neural network classifier, when classified  DFA feature, with sensitivity 0.9445 and specificity 0.9715.

Keywords: Electrohysterogram Uterine; EMG; Sensitivity

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