Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Fard, Seyed Mehdi Hazrati; Agrawal, Vibhav; Gebali, Fayez; Elmiligi, Haytham; Mamun, Mohammad Saiful Islam

doi:10.3390/s23104727

Cited by 7 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On 20 subjects, the authors attained a mean absolute heart rate (HR) estimation error of 1.47, 3.37 beats per minute, and an average accuracy of 96%. Overall, the proposed approach aims to leverage ECG signals and ensemble Siamese Network (ESN) techniques to develop an authentication system with high accuracy and robustness [ 12 ]. The authors used the ECG-ID and PTB datasets and reported accuracy of 93.6% and 96.8%, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers

Ibrahim,

Abbas,

Daadaa

et al. 2023

Sensors

View full text Add to dashboard Cite

Biometric authentication is a widely used method for verifying individuals’ identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the system can accurately identify and authenticate the user despite these variations is a significant challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D image that visually represents the time-varying frequency content of multiple PPG signals from the same human using the scalogram technique. Afterward, the features fusion approach is developed by combining features from the hybrid convolution vision transformer (CVT) and convolutional mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms for the classification of human identity. This hybrid model has the potential to provide more accurate and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP), F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model’s performance in accurately distinguishing genuine individuals. The results of extensive experiments on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of 97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system. These results suggest that the proposed method performs well in accurately classifying or identifying patterns within the PPG signals to perform continuous human authentication.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers

Ibrahim,

Abbas,

Daadaa

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The Siamese network's strength lies in its ability to learn effectively with a small dataset, which is particularly advantageous for user authentication using EMG data. Moreover, it can achieve high accuracy even when new datasets are introduced through transfer learning [35]. Given the difficulty of achieving accuracy with biosignals such as EMG, the Siamese network is applied to maintain high accuracy despite limited learning data.…”

Section: Siamese Networkmentioning

confidence: 99%

“…This can be applied to classification problems, and the network learns in a way that minimizes the distance between data of the same class and increases the distance between data belonging to different classes. Although it may be challenging to apply to generalized networks, it is well suited for the user authentication environment using EMG signals [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Siamese Neural Network for User Authentication in Field-Programmable Gate Arrays (FPGAs) for Wearable Applications

Choi

2023

Electronics

View full text Add to dashboard Cite

User authentication has traditionally been performed using methods such as passwords or fingerprints. However, passwords have security vulnerabilities, and fingerprints may hinder user convenience. To address these issues, a novel user authentication method based on biosignals, specifically electromyogram (EMG) signals, is proposed. Using biosignals like EMG offers several advantages, including the ability to acquire data without user awareness, independence from the user’s environment, rapid acquisition, and enhanced security. However, one challenge with using EMG signals for authentication has been their relatively low accuracy. In this paper, a neural network is implemented using a small number of parameters (fewer than 7000) to produce a wearable device using biosignals, and user authentication accuracy is secured using the maximal overlap discrete wavelet transform (MODWT) method and the Siamese network. The MODWT method is highly effective for the time and frequency analysis of time series data, and the Siamese network is a representative method for few-shot learning. The proposed neural network is verified using Chosun University’s user authentication dataset, encompassing data from 100 individuals. Finally, this proposed network is implemented on an edge device such as field-programmable gate arrays (FPGAs) so that it can be applied to a wearable user authentication system. By implementing the Siamese network in FPGA-based edge devices, it was possible to secure user authentication performance at 94% accuracy and an authentication speed within 1.5 ms. In the case of accuracy, it is expected to be further improved by using the multimodal technique of biosignals. Also, the proposed system can be easily fabricated for digital integrated chips (ICs).

show abstract

“…In comparison to other biometric modalities, ECG signals offer a higher level of security. The existence of these advantages has led to the enhanced utilization of ECG signals for both identification [42][43] and verification [44][45], resulting in satisfactory experimental outcomes.…”

Section: Introductionmentioning

confidence: 99%

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

Yi,

Si,

Fan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Cited by 7 publications

References 38 publications

A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers

A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers

Siamese Neural Network for User Authentication in Field-Programmable Gate Arrays (FPGAs) for Wearable Applications

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

Contact Info

Product

Resources

About