Among various massive multiple-input multiple-output (MIMO) signal detection schemes, expectation propagation (EP) achieves superior performance in high-dimensional systems with high-order modulations and flexible antenna configurations. However, the inevitable matrix inversion in each iteration of EP brings unbearable computational burden, which hinders the efficient implementation. Several reduced-complexity variants of EP are proposed recently, which effectively alleviate the computational cost but at the expense of unacceptable performance loss. In this paper, a low-complexity massive MIMO detection is first proposed based on approximate EP, which relieves the computational complexity of the exact EP while maintaining the good performance. Particularly, the EP moment matching equations are reformulated to simplify the sequential updating procedure. In addition, an approximation based on the channel-hardening phenomenon is proposed to eliminate the matrix inversion at each iteration. Numerical results show that, for high-dimensional MIMO the proposed detector approaches the exact EP in term of bit-error-rate (BER) by a small number of iterations. No matter with symmetric or asymmetric antenna configuration, it outperforms other EP variants, Gaussian tree approximation, and channel-hardening exploiting message passing. An analysis of computational complexity reveals the high efficiency of the proposed detection compared to the state-of-the-art with flexible antenna configurations.