Review of Fiducial and Non-Fiducial Techniques of Feature Extraction in ECG based Biometric Systems

Hassan, Zeeshan; Gilani, Syed Omer; Jamil, Mohsin

doi:10.17485/ijst/2016/v9i21/94841

Cited by 16 publications

(22 citation statements)

References 22 publications

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“…ECG-based biometric authentication systems can likewise be categorized according to the classifier employed to perform the task of recognition [5], such as knearest neighbor (kNN), linear discriminant analysis (LDA), neural networks, generative model, support vector machine (SVM), and match score classifiers. Notably, all classifierbased recognition methods rely on the so-called feature extraction method [1], [25], [26], where they examine the raw ECG signal to extract some significant features to be applied as input to the classifier. However, transforming the raw ECG signal into a proper feature vector for classification has to be thoughtfully performed and requires significant skillful experience [5].…”

Section: Inputs Of Biometric Traitsmentioning

confidence: 99%

“…Generally, the ECG biometrics process combines the following main components: signal pre-processing and QRS detection, feature extraction, feature selection, feature transformation, and classification [14], [27]. Several methods for ECG-based biometric system for human authentication have been introduced to capture valuable information from the ECG signal [14], [25], [28]. For instance, fiducial methods, non-fiducial methods, and partially fiducial methods [25], [29]- [34].…”

Section: Inputs Of Biometric Traitsmentioning

confidence: 99%

“…Several methods for ECG-based biometric system for human authentication have been introduced to capture valuable information from the ECG signal [14], [25], [28]. For instance, fiducial methods, non-fiducial methods, and partially fiducial methods [25], [29]- [34]. The fiducial-based technique depends on accurate detection of the reference points localization in each heartbeat trace like P wave, QRS complex, and T wave and employs interval, amplitude, angle, and area of these points as the biometric features [25], [29], [32].…”

Section: Inputs Of Biometric Traitsmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the selected success measures and proper structure of the ECG signal database play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

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Section: Inputs Of Biometric Traitsmentioning

confidence: 99%

Section: Inputs Of Biometric Traitsmentioning

confidence: 99%

Section: Inputs Of Biometric Traitsmentioning

confidence: 99%

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A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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“…However, in databases with higher variability, non-fiducial features work better. They deal more efficiently with the higher chances of noise in a greater number of samples [27].…”

Section: Related Workmentioning

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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“… Performance:according to requirements, accuracy, speed, and robustness.  Circumvention: it should counteract to fraudsters [3].…”

mentioning

confidence: 99%

ECG signals for human identification based on fiducial and non-fiducial approaches

Ibrahim¹,

Abdel-Mageid²,

Nada³

et al. 2020

IJACR

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Humans recognize each other according to their various characteristics. For example, a father can recognize his daughter by her face when he meets her and by her voice when she speaks to him. In information technology, biometrics is defined as a method to measure and analyze human body characteristics such as iris, DNA, fingerprints, facial patterns, retina and hand measurements, and so on. For authentication purposes, biometrics is a method of recognizing humans depend on unique physical or behavioral characteristics [1,2]. Not all human traits can be used as a biometric, but it should be characterized by: *Author for correspondence  Universality: all persons must possess the biometric trait.  Distinctiveness: the biometric traits should be unique for each person as DNA.  Permanence: a good biometric trait is invariant or changes slowly over time.  Collectability: a biometric trait should be easily collected quantitatively.In addition, the biometric system should be characterized by: Acceptability: The extent for users to accept the daily using of biometric identifiers.  Performance:according to requirements, accuracy, speed, and robustness.  Circumvention: it should counteract to fraudsters [3].

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Review of Fiducial and Non-Fiducial Techniques of Feature Extraction in ECG based Biometric Systems

Cited by 16 publications

References 22 publications

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

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

ECG signals for human identification based on fiducial and non-fiducial approaches

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