Deep-ECG: Convolutional Neural Networks for ECG biometric recognition

Labati, Ruggero Donida; Muñoz, Enrique; Piuri, Vincenzo; Sassi, Roberto; Scotti, Fabio

doi:10.1016/j.patrec.2018.03.028

Cited by 247 publications

(90 citation statements)

References 19 publications

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“…but also the metric is not comprehensive and the accuracy of only 98% has been reported. Labati et.al [13] also used 2D CNN where raw ECG signal is segmented and fed as an input. The author reported 100% accuracy with a 2.90% equal error rate (EER) while only healthy ECG from the PTB database has been studied.…”

Section: A Feature Extraction Categorymentioning

confidence: 99%

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: A Feature Extraction Categorymentioning

confidence: 99%

ECG Biometric Authentication: A Comparative Analysis

et al. 2020

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“…Considering the value of performing an ECG to determine and monitor heart-related conditions, researchers are consciously deriving new mechanisms to acquire, analyse and utilise ECG data for biometric identification. This is mainly due to the following reasons [21,51]: (1) enables better classification ability due containing relatively large feature set, (2) difficult to spoof, (3) multipurpose applications, (4) less affected by surrounding environment, (5) easy integration into the existing system and (6) does not require any special action from users during data acquisition phase. In one recent work, the authors develop an IoT ECG monitoring system where data is collected using a low-cost ECG monitoring device, shared via an internet connection to cloud servers, where it is processed and made available for healthcare practitioners [117].…”

Section: Electrocardiogram (Ecg) Monitoringmentioning

confidence: 99%

Biometric Systems Utilising Health Data from Wearable Devices

et al. 2020

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Health data is being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also presents opportunities within computer security, specifically w ith b iometric s ystems u sed f or i dentification an d authentication purposes. This paper performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work. CCS Concepts: • Security and privacy → Biometrics.

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“…As for the authors of [21], the feature extraction module is bypassed, using the template of the ECG cycle as input to a feed-forward network, for both feature extraction and classification. CNN architecture is also used in this context, in the works of [22,23], this architecture is explored in both authentication and authentication biometric processes. These fiducial systems feed their networks with the average cycle of the signal, based on the neighborhood of the R peak of the ECG signal.…”

Section: Related Workmentioning

confidence: 99%

ECG Biometrics Using Deep Learning and Relative Score Threshold Classification

Belo

Bento

Silva

et al. 2020

Sensors

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The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation.

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Deep-ECG: Convolutional Neural Networks for ECG biometric recognition

Cited by 247 publications

References 19 publications

ECG Biometric Authentication: A Comparative Analysis

ECG Biometric Authentication: A Comparative Analysis

Biometric Systems Utilising Health Data from Wearable Devices

ECG Biometrics Using Deep Learning and Relative Score Threshold Classification

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