Off-Person ECG Biometrics Using Spatial Representations and Convolutional Neural Networks

Ciocoiu, Iulian B.; Cleju, Nicolae

doi:10.1109/access.2020.3042547

Cited by 9 publications

(5 citation statements)

References 50 publications

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“…Although method [27] reported 100% accuracy, the authors used ECG data of 21 subjects which makes the use of this method very limited due to the small data size. The method of Ciocoiu et al [31], which is based on converting the ECG heartbeat segments into images and utilizing the CNN for classification, has slightly exceeded our performance. However, the performance of method [31] considering the variability of ECG features among multiple records was not reported.…”

Section: Discussionmentioning

confidence: 63%

“…The method of Ciocoiu et al [31], which is based on converting the ECG heartbeat segments into images and utilizing the CNN for classification, has slightly exceeded our performance. However, the performance of method [31] considering the variability of ECG features among multiple records was not reported. In comparison, this paper presents a contribution that is based on MODWT to address the variability of ECG features.…”

Section: Discussionmentioning

confidence: 63%

“…(1) Addressing the variability of ECG features at the preprocessing stage by decomposing the signal and performing data filtering methods. Unlike the previous works where the processing stage involved noise removal and signal correction [26,[30][31][32], in this paper we focus on partitioning the signal variability according to its wavelet components. The variability of the ECG features can be analyzed by decomposition of the signal into its wavelet components [39].…”

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: Discussionmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 63%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

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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“…The authors proved that the LSTM model captures intra-beat fluctuations for smaller ECG segments more accurately and reported overall accuracy of 93.11% for the ECGID database. Ciocoiu et al [69] proposed a convolutional neural network with four different types of ECG signal spatial representations as input. The actual techniques that were utilised in the process of transforming the initial time series into 2D and 3D images are based on a modified version of the Continuous Wavelet Transform (S-Transform).…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Technique for Biometric Authentication Using ECG Beat Template Matching

et al. 2023

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An electrocardiogram (ECG) is a unique representation of a person’s identity, similar to fingerprints, and its rhythm and shape are completely different from person to person. Cloning and tampering with ECG-based biometric systems are very difficult. So, ECG signals have been used successfully in a number of biometric recognition applications where security is a top priority. The major challenges in the existing literature are (i) the noise components in the signals, (ii) the inability to automatically extract the feature set, and (iii) the performance of the system. This paper suggests a beat-based template matching deep learning (DL) technique to solve problems with traditional techniques. ECG beat denoising, R-peak detection, and segmentation are done in the pre-processing stage of this proposed methodology. These noise-free ECG beats are converted into gray-scale images and applied to the proposed deep-learning technique. A customized activation function is also developed in this work for faster convergence of the deep learning network. The proposed network can extract features automatically from the input data. The network performance is tested with a publicly available ECGID biometric database, and the proposed method is compared with the existing literature. The comparison shows that the proposed modified Siamese network authenticated biometrics have an accuracy of 99.85%, a sensitivity of 99.30%, a specificity of 99.85%, and a positive predictivity of 99.76%. The experimental results show that the proposed method works better than the state-of-the-art techniques.

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“…Subsequently, a random forest (RF) classifier was utilized, achieving notable results with 97.92% accuracy in identifying individuals on the ECG-ID database. Moreover, Ciocoiu et al [17] employed a modified version of the Continuous Wavelet Transform known as the S-Transform, along with the Gramian Angular Field, recurrence plot, and state-space representations, to convert one-dimensional signals into two-dimensional or three-dimensional images. Their approach achieved an impressive identification accuracy of 98.6% on the CTBHi short-term database.…”

Section: Related Workmentioning

confidence: 99%

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

Yi,

Si,

Fan

et al. 2024

IEEE Access

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In recent years, there has been an increasing focus on privacy and security among individuals. Biometric systems are highly regarded for their strong resistance to counterfeiting. Among various biometric features, ECG signals are difficult to falsify and are less attack-prone. However, as the time interval between signal acquisitions increases, the dissimilarities between individual ECG signals become more pronounced, making it difficult for many studies to achieve satisfactory recognition results in multi-session recognition. Furthermore, some studies encountered challenges in extracting crucial features from the ECG signal, which posed difficulties for identification experiments. To address the above challenge, this study proposes a novel attention-enhanced domain adaptive feature fusion network. Firstly, the network employs a multi-branch architecture to extract essential features from various dimensions of the ECG signal. Secondly, it incorporates the proposed weight fusion adaptive attention mechanism to further emphasize the features of heartbeats that contribute to recognition. Additionally, domain adaptive technology is employed to mitigate differences in feature distribution of ECG signals across sessions, thereby enhancing the model's generalization capability. Finally, four well-known databases, the ECG-ID database, PTB database, CYBHi database, and Heartprint database, were utilized to evaluate the performance of the model. These databases predominantly comprise individuals with multiple records, fulfilling the prerequisites for multi-session recognition. The model achieved recognition results of 96.31%, 73.79%, 77.80%, and 54.78% on these four databases, respectively, surpassing related studies in multisession recognition scenarios.INDEX TERMS Electrocardiogram, biometrics, multi-branch architecture, weight fusion adaptive attention mechanism, domain adaptation.

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Off-Person ECG Biometrics Using Spatial Representations and Convolutional Neural Networks

Cited by 9 publications

References 50 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)

A Deep Learning Technique for Biometric Authentication Using ECG Beat Template Matching

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

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