Physiological signal based biometric analysis has recently attracted attention as a means of meeting increasing privacy and security requirements. The real-time nature of an electrocardiogram (ECG) and the hidden nature of the information make it highly resistant to attacks. This paper focuses on three major bottlenecks of existing deep learning driven approaches: the lengthy time requirements for optimizing the hyperparameters, the slow and computationally intense identification process, and the un-stable and complicated nature of ECG acquisition. We present a novel deep neural network framework for learning human iden-tification feature representations directly from ECG time series. The proposed framework integrates deep bidirectional long short-term memory (BLSTM) and adaptive particle swarm optimization (APSO). The overall approach not only avoids the inef-ficient and experience-dependent search for hyperparameters, but also fully exploits the spatial information of ordinal local fea-tures and the memory characteristics of a recognition algorithm. The effectiveness of the proposed approach is thoroughly eval-uated in two ECG datasets, using two protocols, simulating the influence of electrode placement and acquisition sessions in identification. Comparing four recurrent neural network structures and four classical machine learning and deep learning algo-rithms, we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series. The ex-perimental results demonstrated an average identification rate of 97.71％, 99.41％, and 98.89％ in training, validation, and test sets, respectively. Thus, this study proves that the application of APSO and LSTM techniques to biometric human identification can achieve a lower algorithm engineering effort and higher capacity for generalization.