Electrocardiogram Biometrics Using Transformer’s Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification

Chee, Kai Jye; Ramli, Dzati Athiar

doi:10.3390/s22093446

Cited by 11 publications

(5 citation statements)

References 34 publications

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“…The findings indicate that the research model achieves superior recognition results in both modes. Specifically, in the ECG-ID database, the multi-heartbeat recognition results obtained in this study are 3.61% higher than those reported in [40], and in the PTB database, the multi-heartbeat recognition results are 9.63% higher than [40]. Moreover, the multi-heartbeat recognition results in the CYBHi database are 17.8% higher than [20].…”

Section: ) Comparison With Related Research Resultscontrasting

confidence: 50%

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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Section: ) Comparison With Related Research Resultscontrasting

confidence: 50%

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

Yi,

Si,

Fan

et al. 2024

IEEE Access

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“…For heart rate detection and beat classification problems, there are many finely annotated ECG databases. In addition to the three selected ECG databases, we will also consider databases such as the sudden cardiac death Holter database [ 26 ], European ST-T database [ 27 ], and MIT-BIH ST change database [ 28 ] in the future.…”

Section: Ecg Databases Integration Methodologymentioning

confidence: 99%

Arrhythmia classification detection based on multiple electrocardiograms databases

Qi,

Shao,

Shi

et al. 2023

PLoS ONE

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According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.

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“…The temporal separation between biometric evaluations may influence the system’s performance. Chee et al [ 65 ] investigated the influence of different time separations between enrollment and testing data, using PTB [ 36 ] and ECG-ID [ 32 ] databases, with 83.9 days and 5.5 days between acquisitions, respectively. The model achieved accuracies of 64.16% and 92.70%, for long- and short-time separation, respectively, meaning that the model performance drops significantly when the time separation between the enrollment and classification increases.…”

Section: Ecg Acquisition and Databasesmentioning

confidence: 99%

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

Pereira

Conceição

Sencadas

et al. 2023

Sensors

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In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.

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Electrocardiogram Biometrics Using Transformer’s Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification

Cited by 11 publications

References 34 publications

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

ECG Biometrics Based on Attention Enhanced Domain Adaptive Feature Fusion Network

Arrhythmia classification detection based on multiple electrocardiograms databases

Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods

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