Despite the enormous potential applications, non-invasive recordings have not yet made enough satisfaction for fetal disease detection. This is mainly due to the fetal ECG signal is contaminated by the maternal electrocardiograph (ECG) interference, muscle contractions, and motion artifacts. In this paper, we propose a joint multiple subspace-based blind source separation (BSS) approach to extract the fetal heart rate (HR), so that it could greatly reduce the effect of maternal ECG and motion artifacts. The approach relies on the estimation of the coefficient matrix formulated as the tensor decomposition in terms of multiple datasets. Since the objective function takes the coupling information from the stacking of the covariance matrix for multiple datasets into account, estimating the coefficient matrices is fulfilled not only on dependence across multiple datasets, but also can combine the extracted components across four different datasets. Numerical results demonstrate that the proposed method can achieve a high extracted HR accuracy for each dataset, when compared to some conventional methods.