ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning

Hong, Pei-Lun; Hsiao, Jyun-Ya; Chung, Chi-Hsun; Feng, Yao-Min; Wu, Shun-Chi

doi:10.1109/embc.2019.8856916

Cited by 24 publications

(29 citation statements)

References 19 publications

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“…Furthermore, what is common to the literature shown in the tables is that single-channel ECG (one lead of sensor) contains sufficient information to be discriminated between different subjects for the support of biometric recognition. There are different types of feature extraction modalities [8], [15], [18], [19], [27] and various classifiers [12], [21], [23], [24], [28] have been utilized for ECG-based recognition. In the following section, we summarize the methodologies based on the features and classification schemes.…”

Section: Literature Review Of Ecg-based Biometric Methodsmentioning

confidence: 99%

“…Thus, deep learning helps to boost performance by bypassing the aforementioned restrictions. Hong et.al [12] uses a 2D convolutional neural network (CNN) model in which the ECG signal is converted to an image using spatial correlation-based, temporal correlationbased, and raw signal as an input of CNN model. Specifically, the author uses the Inception-v3 model and transfers learning for the implementation.…”

Section: A Feature Extraction Categorymentioning

confidence: 99%

“…A neural network is especially applied in non-linear classification problems. Various types of these classifiers were used in ECG identification, especially the Multilayer Perceptron (MLP) [12]- [14], [20]- [23], [25]- [28], [32], [36], [38], [42], but also the Long short-term memory (LSTM) [17], the Dynamic Time Warping (DTW) [31], the Radial Basis Function Neural Network (RBFNN) [28], [32], and k nearest neighbor (KNN) [28]. Most of the aforementioned classifiers used a similar approach for the loss function, optimization with a different node activation function.…”

Section: B Classification Categorymentioning

confidence: 99%

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ECG Biometric Authentication: A Comparative Analysis

et al. 2020

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Robust authentication and identification methods become an indispensable urgent task to protect the integrity of the devices and the sensitive data. Passwords have provided access control and authentication, but have shown their inherent vulnerabilities. The speed and convenience factor are what makes biometrics the ideal authentication solution as they could have a low probability of circumvention. To overcome the limitations of the traditional biometric systems, electrocardiogram (ECG) has received the most attention from the biometrics community due to the highly individualized nature of the ECG signals and the fact that they are ubiquitous and difficult to counterfeit. However, one of the main challenges in ECG-based biometric development is the lack of large ECG databases. In this paper, we contribute to creating a new large gallery off-the-person ECG datasets that can provide new opportunities for the ECG biometric research community. We explore the impact of filtering type, segmentation, feature extraction, and health status on ECG biometric by using the evaluation metrics. Our results have shown that our ECG biometric authentication outperforms existing methods lacking the ability to efficiently extract features, filtering, segmentation, and matching. This is evident by obtaining 100% accuracy for PTB, MIT-BHI, CEBSDB, CYBHI, ECG-ID, and in-house ECG-BG database in spite of noisy, unhealthy ECG signals while performing five-fold cross-validation. In addition, an average of 2.11% EER among 1,694 subjects is obtained.

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Section: Literature Review Of Ecg-based Biometric Methodsmentioning

confidence: 99%

Section: A Feature Extraction Categorymentioning

confidence: 99%

Section: B Classification Categorymentioning

confidence: 99%

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ECG Biometric Authentication: A Comparative Analysis

et al. 2020

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“…Biel et al's [12,13] works are considered the first attempt to use ECGs for biometric purposes, considering the biometric characteristics of measurability (ease with which the characteristic is obtained), permanence (no change over time), universality (possession of the characteristic by the individual), and uniqueness (no two individuals share the same characteristic) [14][15][16][17]. Since then, many researchers have proposed various ECG-based identification approaches [1,4,[18][19][20][21][22][23][24][25][26][27] using private and/or public databases [28,29].…”

Section: Introductionmentioning

confidence: 99%

“…Feature extraction is needed to provide unique biomarkers for a given ECG signal. Feature extraction methods can be grouped into three main categories: fiducial-based approaches which extract features while preserving the characteristics of the ECG signal, e.g., the amplitudes and intervals of heartbeats [20,31,[37][38][39][40][41][42][43], nonfiducial-based approaches which do not require such precise knowledge of ECG characteristics [44][45][46][47][48][49][50][51][52][53], and hybrid-based approaches [54,55].…”

Section: Introductionmentioning

confidence: 99%

ECG-Based Subject Identification Using Statistical Features and Random Forest

et al. 2019

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In this work, a nonfiducial electrocardiogram (ECG) identification algorithm based on statistical features and random forest classifier is presented. Two feature extraction approaches are investigated: direct and band-based approaches. In the former, eleven simple statistical features are directly extracted from a single-lead ECG signal segment. In the latter, the single-lead ECG signal is first decomposed into bands, and the statistical features are extracted from each segment of a given band and concatenated to form the feature vector. Nonoverlapping segments of different lengths (i.e., 1, 3, 5, 7, 10, or 15 sec) are examined. The extracted feature vectors are applied to a random forest classifier, for the purpose of identification. This study considers 290 reference subjects from the ECG database of the Physikalisch-Technische Bundesanstalt (PTB). The proposed identification algorithm achieved an accuracy rate of 99.61% utilizing the single limb lead (I) with the band-based approach. A single chest lead (V1), augmented limb lead (aVF), and Frank’s lead (Vx) achieved an accuracy rate of 99.37%, 99.76%, and 99.76%, respectively, using the same approach.

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A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

Musa

Gital

Aljojo

et al. 2022

J Ambient Intell Human Comput

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The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG.The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm.

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ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning

Cited by 24 publications

References 19 publications

ECG Biometric Authentication: A Comparative Analysis

ECG Biometric Authentication: A Comparative Analysis

ECG-Based Subject Identification Using Statistical Features and Random Forest

A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram

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