Abstract
Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/low-frequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major preprocessing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the employed success measures and appropriate constitution of the ECG signal database also play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.
Highlights
N OWADAYS, biometric recognition systems as a key form of user authentication are used increasingly in different fields and applications such as smartphones, banks, websites, and airports, as a unique substitute to conventional authentication techniques (i.e., keys and personal identification numbers (PINs) [1]–[5], based on the required level of security
Since deep learning is considered a promising method for analyzing and classifying ECG data for biometric recognition, we present an in-depth review on ECG signal classification with deep learning methods, under the categories of deep supervised learning (DSL), deep semi-supervised learning (DSSL), and deep unsupervised learning (DUL), which can be used for analyzing and classifying ECG data for biometric systems
Achieved 2.90% equal error rate (EER) using the PTB database for authentication Achieved 15.60% EER using the CYBHi database Achieved 20.48% EER using the CYBHi database Achieved 1.53% EER using the PTB database Achieved 0.27% EER using the CYBHi database Achieved an average identification rate of 93.5% evaluated on eight ECG PhysioNet (CEBSDB, WECG, FANTASIA, NSRDB, STDB, MITDB, AFDB, and VFDB) datasets
Summary
N OWADAYS, biometric recognition systems as a key form of user authentication are used increasingly in different fields and applications such as smartphones, banks, websites, and airports, as a unique substitute to conventional authentication techniques (i.e., keys and personal identification numbers (PINs) [1]–[5], based on the required level of security. In [80], a review of ECG-based authentication systems is presented and topics discussed include the existing ECG benchmarks, fiducial and non-fiducial features authentication and methods, and data mining classification techniques. To the best of the authors’ knowledge, a comprehensive survey that addresses several topics on ECG biometric systems based on preprocessing, feature extraction, feature selection, feature transformation, deep learning for ECG classification, databases are still missing. This survey paper proposes to fill this gap by presenting a comprehensive study on several ECG-based biometric recognition topics to allow interested readers to reach the sought knowledge rapidly. Many unrelated articles stated some of the keywords in their introduction sections or related work sections, which gave rise to a large initial set of articles
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