This Fetal electrocardiogram is a standard method to identify and diagnose fetal diseases. Therefore, effective techniques are needed to monitor fetal conditions during pregnancy and delivery. Meanwhile, obtaining the fetal electrocardiogram (FECG) signal, which contains the electrical activity of the fetal heart, has great importance, Of course, this signal is contaminated with many noises and disturbances that the most important of them is the mother's electrocardiogram signal. Inherently, the NI-ECG signal contains the maternal ECG signal, which has a larger amplitude than the fetal ECG signal. Therefore, it is not easy to detect the fetal QRS complex in order to control the condition of the fetus and prevent congenital defects. According to the above explanations, in this article, a deep learning approach based on convolutional neural network is proposed to separate the electrocardiogram signals of the mother from the fetus without separating the mother's ECG signal. The proposed algorithm is able to reliably detect the fetal QRS complex. Also, in addition to not needing feature extraction steps, it has been able to show more suitable performance than the best methods proposed in previous researches in terms of detection accuracy.