ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison

Pelc, Mariusz; Khoma, Yuriy; Khoma, Volodymyr

doi:10.3390/s19102350

Cited by 31 publications

(20 citation statements)

References 15 publications

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“…This study resulted in an accuracy of up to 98.87% [4]. Ahmed et al [5], Sellami et al [6], and Mariusz [7] also simulate an ECG biometric. They use the MIT-BIH database.…”

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confidence: 87%

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ECG Based Biometric using Wavelet Packet Decomposition

Hadiyoso,

Rizal,

Wijayanto

2019

IJEAT

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Biometric technology has been commonly used for authentication. Fingerprint or iris become one of the biometrics that is widely applied. However, this type of biometrics tends to be easily falsified and damaged. So it is misused for manipulating actions and even crime. Therefore a new biometric method is needed to overcome this problem. One potential modality is biometrics based on an electrocardiogram (ECG) signal. This research simulates a one-lead ECG waveform for person authentication. ECG waves were taken from eleven healthy adult volunteers with a length of 60 seconds. ECG waves from each person are segmented into 10 sections so that a total of 110 ECG waves are used for person authentication simulations. All noise of the ECG waves was removed using a bandpass filter to reduce artifacts and high-frequency noise. Wavelet packet decomposition (3 Level) was applied to decompose the signal in several intrinsic parts so that typical wave information can be retrieved. Entropy-based feature extraction applied to all decomposed signals. A total of 14 entropy features have been calculated and used as predictors in the classification process. Validation and performance tests are carried out by cross-validation combined with linear discriminant analysis and support vector machines with five scenarios. The proposed method provides the highest accuracy of 71.8% using discriminant analysis and cubic support vector machine. The best accuracy value was achieved if all entropy features from all wavelet decomposition levels are used as predictors in the classification process. This research is expected to be a reference that ECG has the potential to become a future biometric modality

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“…This study resulted in an accuracy of up to 98.87% [4]. Ahmed et al [5], Sellami et al [6], and Mariusz [7] also simulate an ECG biometric. They use the MIT-BIH database.…”

mentioning

confidence: 87%

“…Research [5], [6] achieved an accuracy of >90%, each of these studies using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). Mariusz proposed linear discriminant analysis and claimed to have high accuracy [7]. The primary purpose of some of the studies mentioned is to choose the best method to produce high accuracy.…”

mentioning

confidence: 99%

ECG Based Biometric using Wavelet Packet Decomposition

Hadiyoso,

Rizal,

Wijayanto

2019

IJEAT

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“…These structures have already been used as a classifier in ECG biometrics, achieving good performances [29,30]. Despite of the simplicity of this artificial neural network, it is considered a promising algorithm in the context of ECG biometrics [31]. MLP networks are applied in supervised learning and they have three main parts: input, output, and hidden layers, as represented in Figure 3.…”

Section: Classificationmentioning

confidence: 99%

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

et al. 2020

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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.

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“…Nowadays modeling (Bender, 2012) and experimental studies (Wang et al, 2019) as well as simplify the structure of objects (Shu and Kochan, 2013) are often used to solve various problems in science (Bender, 2012), technology (Irfan et al, 2019), medicine (Pelc et al, 2019) and industry (Hreha et al, 2015;Wojciechowski et al, 2018). They make it possible, by simplifying the objects or identifying their essential properties to investigate the behavior of complex devices and systems, and based on these studies, build theories that will suggest the optimum way of operation or predict their behavior under real operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Constructing Reference Plans of Two-Criteria Multimodal Transport Problem

Przystupa

Zhang

Zabolotnii

et al. 2021

Transport and Telecommunication Journal

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The object of this study is a multicriteria transport problem, being stated for availability of several means of cargo delivery, meaning a multimodal transport problem. The optimization criteria of the multimodal transport problem described above are two objective functions of minimizing total transportation costs and level of transport risks. Three types of transport were selected for research: automobile, rail and river (inland waterway). The results of the study lay the foundation for development of a new valid algorithm for solving multimodal transport problems like multi-criteria optimization ones. The main advantage of such an algorithm lies in its higher potential convergence rate compared to classical numerical optimization methods, which now are predominantly used to solve the problems of this type. This advantage may not be decisive, but it appears to be at least quite an important argument when choosing the method of realization for two-criteria multimodal transport problems earlier considered, especially, in case of a large dimension. Moreover, the algorithm described in the work can be applied to similar problems with any number of types of transport and optimization criteria.

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ECG Signal as Robust and Reliable Biometric Marker: Datasets and Algorithms Comparison

Cited by 31 publications

References 15 publications

ECG Based Biometric using Wavelet Packet Decomposition

ECG Based Biometric using Wavelet Packet Decomposition

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

Constructing Reference Plans of Two-Criteria Multimodal Transport Problem

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