Biometrie identification from raw ECG signal using deep learning techniques

Więcław, Łukasz; Khoma, Yuriy; Fałat, Paweł; Sabodashko, Dmytro; Herasymenko, Veronika

doi:10.1109/idaacs.2017.8095063

Cited by 41 publications

(26 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this regard, different biometrics technologies such as palm vein readers, fingerprint, ECG, and iris scanners [105], and face recognition have great potential to be deployed in healthcare systems. It is very common to use ML/DL techniques for building healthcare biometric systems, which are themselves vulnerable to security and privacy attacks [106], [107], [108]. For example, an adversary can easily evade a face recognition system that is deployed in a restricted area to restrain unintended access for security purposes.…”

Section: B the Security Of Ml: An Overviewmentioning

confidence: 99%

Secure and Robust Machine Learning for Healthcare: A Survey

Qayyum

Qadir

Bilal

et al. 2021

IEEE Rev. Biomed. Eng.

386

144

View full text Add to dashboard Cite

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from onedimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images. Notwithstanding the impressive performance of ML/DL, there are still lingering doubts regarding the robustness of ML/DL in healthcare settings (which is traditionally considered quite challenging due to the myriad security and privacy issues involved), especially in light of recent results that have shown that ML/DL are vulnerable to adversarial attacks. In this paper, we present an overview of various application areas in healthcare that leverage such techniques from security and privacy point of view and present associated challenges. In addition, we present potential methods to ensure secure and privacy-preserving ML for healthcare applications. Finally, we provide insight into the current research challenges and promising directions for future research.

show abstract

Section: B the Security Of Ml: An Overviewmentioning

confidence: 99%

Secure and Robust Machine Learning for Healthcare: A Survey

Qayyum

Qadir

Bilal

et al. 2021

IEEE Rev. Biomed. Eng.

386

144

View full text Add to dashboard Cite

show abstract

“…The extracted features from these time-frequency matrices are fed into a feed-forward network for classification. 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.…”

Section: Related Workmentioning

confidence: 99%

ECG Biometrics Using Deep Learning and Relative Score Threshold Classification

Belo

Bento

Silva

et al. 2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…It allows us to start from the most ideal case, due to the precision these devices provide and work towards more complex scenarios. As an alternative, other researchers develop their prototypes, adding more challenges in the system's implementation [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

QRS Differentiation to Improve ECG Biometrics under Different Physical Scenarios Using Multilayer Perceptron

et al. 2020

View full text Add to dashboard Cite

Currently, machine learning techniques are successfully applied in biometrics and Electrocardiogram (ECG) biometrics specifically. However, not many works deal with different physiological states in the user, which can provide significant heart rate variations, being these a key matter when working with ECG biometrics. Techniques in machine learning simplify the feature extraction process, where sometimes it can be reduced to a fixed segmentation. The applied database includes visits taken in two different days and three different conditions (sitting down, standing up after exercise), which is not common in current public databases. These characteristics allow studying differences among users under different scenarios, which may affect the pattern in the acquired data. Multilayer Perceptron (MLP) is used as a classifier to form a baseline, as it has a simple structure that has provided good results in the state-of-the-art. This work studies its behavior in ECG verification by using QRS complexes, finding its best hyperparameter configuration through tuning. The final performance is calculated considering different visits for enrolling and verification. Differentiation in the QRS complexes is also tested, as it is already required for detection, proving that applying a simple first differentiation gives a good result in comparison to state-of-the-art similar works. Moreover, it also improves the computational cost by avoiding complex transformations and using only one type of signal. When applying different numbers of complexes, the best results are obtained when 100 and 187 complexes in enrolment, obtaining Equal Error Rates (EER) that range between 2.79–4.95% and 2.69–4.71%, respectively.

show abstract

Biometrie identification from raw ECG signal using deep learning techniques

Cited by 41 publications

References 6 publications

Secure and Robust Machine Learning for Healthcare: A Survey

Secure and Robust Machine Learning for Healthcare: A Survey

ECG Biometrics Using Deep Learning and Relative Score Threshold Classification

QRS Differentiation to Improve ECG Biometrics under Different Physical Scenarios Using Multilayer Perceptron

Contact Info

Product

Resources

About