eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2018-03-25 11 1 369 374 10.13005/bpj/1381 19013 article Shaniba Asmi P. 1 Kamalraj Subramaniam 1 Nisheena V. Iqbal 1 Department of Electronics and Communication Engineering, Karpagam university, Coimbatore India. 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. https://biomedpharmajournal.org/vol11no1/classification-of-fractal-features-of-uterine-emg-signal-for-the-prediction-of-preterm-birth/ Electrohysterogram Uterine EMG  Sensitivity