Multi-scale differential feature for ECG biometrics with collective matrix factorization

Wang, Kuikui; Yang, Geng; Huang, Yu-Wen; Yin, Yilong

doi:10.1016/j.patcog.2020.107211

Cited by 31 publications

(17 citation statements)

References 30 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%

“…During authentication, the score is compared to a predefined threshold, and the claimed identity is accepted, if the score is greater. There are different types of classifiers such as Euclidean distance, support vector machines (SVMs), dynamic time warping (DTW), and hamming distance utilized in the literature [8], [15], [18], [19], [21], [23], [24], [27], [35], [41], [44], [46]. ECG identification Most of the deep learning approaches for ECG biometric systems have been studied in the literature review used a fully connected layer and softmax for a classifier.…”

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: B Classification Categorymentioning

confidence: 99%

ECG Biometric Authentication: A Comparative Analysis

et al. 2020

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“…Nowadays, several methods already exist to recognize MI, including electrocardiogram (ECG), biomarkers, imaging technique, or defined by pathology. Yet, the non-invasive ECG is the most economical and widely used one for the sake of immediate treatment strategies among them [ 3 , 4 , 5 , 6 , 7 ]. Performing ECG analysis manually may not merely be time-consuming but leads to inter-observer variability [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Jian

Ger

Lai

et al. 2021

Sensors

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Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

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“…The collective matrix factorization (CMF) can transform the original space into the latent semantics space and remove redundant information, and many CMF-based methods are proposed for biometric recognition [17] [18] [19]. However, the existing CMF-based methods learn semantic representations from cross-domains, without considering multiple features.…”

Section: Introductionmentioning

confidence: 99%

Multi-Feature Sparse Representations Learning via Collective Matrix Factorization for ECG Biometric Recognition

Liu

Huang

et al. 2021

IEEE Access

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Electrocardiogram (ECG) signal is a promising biometric trait, and many methods have been proposed for ECG biometric recognition. However, it is challenging to design a robust and precise method to improve the recognition performance of ECG signals with noise and signal variation. We present a multi-feature sparse representations learning model via collective matrix factorization for ECG biometric recognition, MSRCMF for short. First, we extract one-dimensional local binary pattern (1D-LBP), shape and wavelet features of ECG signals and then obtain their sparse representations. Second, to extract discriminative information and preserve the intra-and inter-subject similarities, we leverage the collective matrix factorization on multiple sparse representations and the label information to obtain the latent semantic space. At last, we can recognize the ECG signals in the learned semantic space. Extensive experiments on four ECG databases show that MSRCMF can achieve competitive performance compared to state-of-the-art methods.

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Multi-scale differential feature for ECG biometrics with collective matrix factorization

Cited by 31 publications

References 30 publications

ECG Biometric Authentication: A Comparative Analysis

ECG Biometric Authentication: A Comparative Analysis

Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Multi-Feature Sparse Representations Learning via Collective Matrix Factorization for ECG Biometric Recognition

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