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 unstable and complicated nature of ECG acquisition. We present a novel deep neural network framework for learning human identification 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 inefficient and experience-dependent search for hyperparameters, but also fully exploits the spatial information of ordinal local features and the memory characteristics of a recognition algorithm. The effectiveness of the proposed approach is thoroughly evaluated 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 algorithms, we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series. The experimental 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.

基于生理信号的生物特征分析最近作为满足日益增长的隐私和安全要求的一种手段引起了人们的关注。心电图 (ECG) 的实时特性和信息的隐藏特性使其对攻击具有很强的抵抗力。本文重点关注现有深度学习驱动方法的三大瓶颈：优化超参数所需的时间长、识别过程缓慢且计算量大，以及心电图采集的不稳定和复杂性。我们提出了一种新颖的深度神经网络框架，用于直接从 ECG 时间序列中学习人类识别特征表示。所提出的框架集成了深度双向长短期记忆（BLSTM）和自适应粒子群优化（APSO）。整体方法不仅避免了对超参数的低效和依赖经验的搜索，而且充分利用了有序局部特征的空间信息和识别算法的记忆特性。所提出方法的有效性在两个 ECG 数据集中进行了彻底评估，使用两种协议，模拟电极放置和采集会话在识别中的影响。比较四种循环神经网络结构和四种经典的机器学习和深度学习算法，我们证明了所提出的算法在最小化时间序列过拟合和自学习方面的优越性。实验结果表明，训练集、验证集和测试集的平均识别率分别为 97.71%、99.41% 和 98.89%。因此，