Currently, ECG-based authentication is considered highly promising in terms of user identification for smart healthcare systems because of its inimitability, suitability, accessibility and comfortability. However, it is a great challenge to improve the authentication accuracy, especially for scenarios that include a large number of users. Thus, this paper proposes a parallel ECG-based authentication called PEA. Specifically, this paper proposes a hybrid ECG feature extraction method that integrated fiducial- and non-fiducial-based features to extract more comprehensive ECG features and thereby improve the authentication stability. Furthermore, this paper proposes a parallel ECG pattern recognition framework to improve the recognition efficiency in multiple ECG feature spaces. Through the experiments, the performance of the proposed authentication is verified.