Antenatal fetal health monitoring primarily depends on the signal analysis of abdominal or transabdominal electrocardiogram (ECG) recordings. The noninvasive approach for obtaining fetal heart rate (HR) reduces risks of potential infections and is convenient for the expectant mother. However, in addition to strong maternal ECG presence, undesirable signals due to body motion activity, muscle contractions, and certain bio-electric potentials degrade the diagnostic quality of obtained fetal ECG from abdominal ECG recordings. In this paper, we address this problem by proposing an improved framework for estimating fetal HR from non-invasively acquired abdominal ECG recordings. Since the most significant contamination is due to maternal ECG, in the proposed framework, we rely on neural network autoencoder for reconstructing maternal ECG. The autoencoder endeavors to establish the nonlinear mapping between abdominal ECG and maternal ECG thus preserving inherent fetal ECG artifacts. The framework is supplemented with an existing blind-source separation (BSS) algorithm for post-treatment of residual signals obtained after subtracting reconstructed maternal ECG from abdominal ECG. Furthermore, experimental assessments on clinically-acquired subjects' recordings advocate the effectiveness of the proposed framework in comparison with conventional techniques for maternal ECG removal.