Heart biometrics based on ECG signal by sparse coding and bidirectional long short-term memory
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Heart biometrics based on ECG signal by sparse coding and bidirectional long short-term memory Yefei Zhang 1
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& Zhidong Zhao & Yanjun Deng & Xiaohong Zhang & Yu Zhang
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Received: 5 March 2020 / Revised: 21 July 2020 / Accepted: 12 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Physiological signal-based biometrics are gaining increasing attention in the context of increasing privacy and security requirements. This paper proposes a novel electrocardiogram (ECG)-based algorithm to be used for human identification by integrating multiple local feature vectors with sparse-constraint-based sparse coding (SCSC) and bidirectional long short-term memory (BLSTM). Three local feature vectors of ECG signals: morphology characteristics in the time domain, instantaneous characteristics in the frequency domain, and phase spectral characteristics in the phase domain are constructed. Sparsity constraints to model this relationship are imposed because ECGs show high inter-class similarity and subtle intra-class differences in these three domains, and traditional sparse coding (SC) can only learn from a single dictionary. This paper joints optimization of the summed reconstruction error, the sparsity constraints of the correlations and the differences between the feature vectors, proposed the SCSC algorithm. Via this approach, the overlap problem of local feature vectors is solved and a lightweight and interpretable feature vector is obtained. Additionally, the BLSTM-based deep neural network model is supplemented for exploring the spatial information of the reconstructed feature vectors, and a more representative and discriminative signal feature representation is obtained. Comparing five classical machine learning and deep learning algorithms within 360 public samples, using two protocols, we show that, in addition to multiscale information extraction, joint encoding of the correlations and differences between local feature vectors is critically important for feature discrimination. The experimental results demonstrated a high identification accuracy of 99.44%, indicating that the proposed algorithm has practical utility in network information security. Keywords ECG biometrics . Human identification . Long short-term memory (LSTM) . Sparse coding (SC)
* Zhidong Zhao [email protected]
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College of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou 300318, People’s Republic of China
Multimedia Tools and Applications
1 Introduction In this information era, network information security is of substantial significance to individuals, enterprises and countries. Traditional identification methods have numerous security problems, including information loss and theft. Expert and intelligent biometrics technologies, which integrate information technology and biotechnology, have been actively studied and applied in fields such as ECG signals [6] analysis. With the development of small volume, easy integration and low power biosignal acquisition chips, the
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