  Fetal motion discernment utilizing spectral images extracted from
accelerometric data incident on pregnant mothers abdomen has gained substantial
attention in the state-of-the-art research. It is an essential practice to
avoid adverse scenarios such as stillbirths and intrauterine growth
restrictions. However, this endeavor of ensuring fetus safety has been arduous
due to the existence of random noise in medical images. This novel research is
an in depth approach to analyze how the interference of different noise
variations affect the retrieval of information in those images. For that, an
algorithm employing auto-encoder-based deep learning was modeled and the
accuracy of reconstruction of the STFT images mitigating the noise has been
measured examining the loss. From the results, it is manifested that even a
substantial addition of the Super-Gaussian noises which have a higher
correlation of the frequencies possessed by the Fetal movement images can be
restored successfully with the slightest error.
