A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks

• Published in: IEEE access Vol. 7; pp. 145395 - 145405
• Main Authors: Lynn, Htet Myet, Pan, Sung Bum, Kim, Pankoo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 1D-convolutional neural networks; Accuracy; Arrhythmia; Artificial neural networks; bidriectional recurrent neural networks; Biological system modeling; Biometric identification; Biometrics; biometrics classification; Classification; Computer architecture; Datasets; ECG signals; Electrocardiography; Feature extraction; gated recurrent unit; Heart; Logic gates; Machine learning; Neural networks; Recurrent neural networks; Representations; Sequences; signal processing; user identification
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2939947

In this paper, we propose a deep Recurrent Neural Networks (RNNs) based on Gated Recurrent Unit (GRU) in a bidirectional manner (BGRU) for human identification from electrocardiogram (ECG) based biometrics, a classification task which aims to identify a subject from a given time-series sequential data. Despite having a major issue in traditional RNN networks which they learn representations from previous time sequences, bidirectional is designed to learn the representations from future time steps which enables for better understanding of context, and eliminate ambiguity. Moreover, GRU cell in RNNs deploys an update gate and a reset gate in a hidden state layer which is computationally efficient than a usual LSTM network due to the reduction of gates. The experimental results suggest that our proposed BGRU model, the combination of RNN with GRU cell unit in bidirectional manner, achieved a high classification accuracy of 98.55%. Various neural network architectures with different parameters are also evaluated for different approaches, including one-dimensional Convolutional Neural Network (1D-CNN), and traditional RNNs with LSTM and GRU for non-fiducial approach. The proposed models were evaluated with two publicly available datasets: ECG-ID Database (ECGID) and MIT-BIH Arrhythmia Database (MITDB). This paper is expected to demonstrate the feasibility and effectiveness of applying various deep learning approaches to biometric identification and also evaluate the effect of network performance on classification accuracy according to the changes in percentage of training dataset.