Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals

Lee, Jae-Neung; Kwak, Keun-Chang

doi:10.1109/access.2019.2904095

Cited by 25 publications

(10 citation statements)

References 58 publications

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“…As a result, the authors in [27] reported 100% identification accuracy using ECG data of 21 subjects. Lee et al [28] proposed an algorithm based on a time frequency representation of the ECG data. Both the robust principal components analysis network (RPCANet) and DWT methods were utilized for feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

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The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

Biran

Jeremić

2023

DMHT

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Electrocardiography (ECG) has been a subject of research interest in human identification because it is a promising biometric trait that is believed to have discriminatory characteristics. However, features of ECGs that are recorded at different times are often likely to vary significantly. To address the variability of ECG features over multiple records, we propose a new methodology for human identification using ECGs recorded on different days. To demonstrate the applicability of our method, we use the publicly available ECG ID dataset. The main goal of this work is to extract the most significant and discriminative wavelet components of the ECG signal, followed by utilizing the ECG spectral change for human identification using multi-level filtering technique. Our proposed multi-channel identification system is based on using the Maximal Overlap Discrete Wavelet Transform (MODWT) and its inverse (the IMODWT) to create multiple filtered ECG signals. The discriminative feature that we utilize for human identification is based on modeling the dynamic change of the frequency components in these multiple filtered signals. To reach the best possible identification performance, we use the Weighted Majority Voting Method (WMVM) for ECG classification. We evaluated the robustness of our proposed method over several random experiments and obtained 92.29% average identification accuracy, 0.9495 precision, 0.9229 recall, 0.0771 FRR and 0.0013 FAR. These results indicate that filtering some of the ECG wavelet components along with performing data fusion technique can be utilized for human identification.

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Section: Literature Reviewmentioning

confidence: 99%

“…Both the robust principal components analysis network (RPCANet) and DWT methods were utilized for feature extraction. The support vector machine (SVM) was used for ECG classification [28]. Similarly, Arwa et al [29] introduced a wavelet-based method which utilizes the ECG power and energy features for personal identification.…”

Section: Literature Reviewmentioning

confidence: 99%

The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

Biran

Jeremić

2023

DMHT

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“…Besides, the residual network is employed to present an ECG biometric authentication method, which can improve the generalization ability of the method on different ECG signals sampled in the different environments for the matching task [29]. In [89], the features are extracted by the principal component analysis network (PCANet) and then a robust Eigen ECG network (REECGNet) is used on time-frequency representations of ECGs for personal identification. Gated Recurrent Unit (GRU) based method in a bidirectional manner is proposed for human identification from ECG [111].…”

Section: Human Identificationmentioning

confidence: 99%

“…[129] proposed a modified U-net to handle varied length ECG for classification and R-peak detection. PCANet [22], an image classification model working with the help of cascaded principal component analysis (PCA), binary hashing, and blockwise histograms, is modified for biometric human identification task in non-stationary ECG noise environment [89]. But its effect remains to be evaluated with the state-of-thearts baselines introduced in the above sections.…”

Section: Fc and Othersmentioning

confidence: 99%

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review

Hong¹,

Zhou²,

Shang³

et al. 2020

Preprint

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Background: Electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results predictive healthcare tasks using ECG signals. Objective: This paper conducts a systematic review of deep learning methods on ECG data from both model and application perspectives. Methods: We extracted papers that deploy deep learning (deep neural networks) models on ECG data that published between January 1st 2010 and February 29th 2020 from Google Scholar, PubMed, and DBLP. We then analyze them in three aspects, including task, model, and data. Last we discuss open challenges and unsolved problems in this area. Results: The total number of papers is 191; among them, 108 papers are published after the year 2019. Almost all kinds of common deep learning architectures have been used in ECG analytics tasks like disease detection/classification, annotation/localization, sleep staging, biometric human identification, denoising, and so on. Conclusion:The number of works about deep learning on Electrocardiogram data is growing explosively in recent years. Indeed, these works have achieved a far better performance in terms of accuracy. However, there are some new challenges and problems like interpretability, scalability, efficiency, which need to be addressed and paid more attention. Moreover, it is also worth investigating by discovering new interesting applications from both the dataset view and the method view. Significance: This paper summarizes existing deep learning methods on modeling ECG data from multiple views while also point out existing challenges and problems while it can become a potential research direction in the future.

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“…Li et al proposed a genetic algorithm-back propagation neural network (GA-BPNN) classification method based on wavelet packet decomposition feature extraction [18]. Lee and Kwak introduced a time-frequency representation (WT method) of ECG signal for personal identification [19]. Venkatesan et al developed a remote healthcare system where discrete wavelet transform (DWT) is implemented on the heart rate variability (HRV) feature extraction [20].…”

Section: Introductionmentioning

confidence: 99%

Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network

Sun

Zeng

He³

2019

IEEE Access

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Electrocardiogram (ECG) signal represents the electrical activity of the heart and playing an increasingly important role for practitioners to diagnose heart diseases. Widely available ECG data and machine learning algorithms present an opportunity to improve the accuracy of automated arrhythmia diagnosis. However, a comprehensive evaluation of morphological arrhythmias for the ECG analysis across a wide variety of diagnostic classes is still a complex task. This paper presents a generic morphological arrhythmias classification method designed for robust and accurate detection of the ECG heartbeat. In this method, the gray-level co-occurrence matrix (GLCM) is employed for features vector description because of its extraordinary statistical feature extraction ability. In addition, the convolutional neural network (CNN) approach is utilized to automatically classification from the generated 3D multi-scale GLCM. Obtained results from the experiments demonstrate that the proposed method in this paper is quite suitable for morphological arrhythmias detection. These findings demonstrate that the GLCM description can efficiently extract the shape features vector for a broad range of distinct arrhythmias from the lead ECGs with high diagnostic performance. The experimental results of the recognition for morphological arrhythmias show the feasibility and effectiveness of the proposed method and could be used to reduce the rate of the misdiagnosed computerized ECG interpretations.INDEX TERMS Morphological arrhythmia, gray-level co-occurrence matrix (GLCM), electrocardiogram (ECG), cardiac arrhythmias classification, convolutional neural network (CNN).

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Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals

Cited by 25 publications

References 58 publications

The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

The Feasibility of Human Identification from Multiple ECGs using Maximal Overlap Discrete Wavelet Transform (MODWT) and Weighted Majority Voting Method (WMVM)

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review

Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network

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