In this paper, two novel and computationally efficient, zero-phase filtering techniques are proposed based on the Riemann-Liouville integral. Thanks to the reverse phase characteristics of backward filtering, an overall zero-phase effect can be achieved by cascading a fractional forward filtering with a fractional backward filtering, and vice versa. The fractional zero-phase filtering can not only effectively suppress the phase distortion in the filtering process but also better enhance the compromise capability between signal denoising and signal information retention than the conventional filtering methods do. The proposed methods are evaluated on Electrocardiogram (ECG) signal, by adding disturbance, random, and white Gaussian noises to visually clean ECG record, and studying SNR and MSE of the filter outputs. The results of the study demonstrate superior performances compared with conventional signal denoising methods, such as Riemann-Liouville integral filtering, Grunwald-Letnikov integral filtering, zero-phase Butterworth filtering, and zero-phase average window filtering.