Individual identification based on chaotic electrocardiogram signals

Chen, Ching-Kun; Lin, Chun-Liang; Chiu, Yen-Ming

doi:10.1109/iciea.2011.5975879

Cited by 26 publications

(31 citation statements)

References 9 publications

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“…Sophisticated technologies can be successfully implemented for many biometric authentication applications such as protection of private documents and reliable access to restricted areas [1]. Recent advances in biomedical and clinical engineering areas not only improve the recognition performance of biometric authentication systems but also provide feasible implementations [2].…”

Section: Introductionmentioning

confidence: 99%

“…One of the most popular methods is to use the distance measure [2], [4], [10], [12], [14] , [21]. Nearest neighbor classifier was also widely used for this purpose [1], [15], [20], [22], [23], [24]. Neural Network [6], and Decision Based Neural Network [13], [14] were effectively applied for person identification as well.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, electrocardiogram (ECG) signals gathered from individuals have been suggested as a biometric for identity verification [1], [5], [6]. ECG signal inherently reserves the electrical properties of the heart.…”

Section: Introductionmentioning

confidence: 99%

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ECG based biometric authentication using ensemble of features

Ergin

Uysal

Günal

et al. 2014

2014 9th Iberian Conference on Information Systems and Technologies (CISTI)

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In this work, the efficacy of various features on electrocardiogram (ECG) based biometric authentication process is thoroughly examined. In particular, the features acquired from temporal analysis, wavelet transformation, power spectral density estimation and QRS-complex detection over ECG signals are considered. These features are employed with two distinct classification algorithms, namely decision tree and Bayes network, specifically for gender, age and identity recognition problems. The biometric authentication framework is evaluated on a benchmark dataset that contains ECG records of 18 healthy people including 5 men, aged 26 to 45, and 13 women, aged 20 to 50. The results of the experimental analysis reveal that if all those features are used in combination rather than individually, better performance is attained for all classifiers in each recognition problem.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ECG based biometric authentication using ensemble of features

Ergin

Uysal

Günal

et al. 2014

2014 9th Iberian Conference on Information Systems and Technologies (CISTI)

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“…It is promising that security is enhanced by using the biometric information transmitted to loT. Studies of 978-1-4799-680 1-5/14/$3l.00 ©20 14 IEEE personal authentication [3][4] [5] using the electrocardiographic wavefonn as one of the biometric authentication methods have been reported. Since electrocardiogram is time variant biologi cal information, spoofing is more difficult than face or finger print based authentication.…”

Section: Introductionmentioning

confidence: 99%

Biometrics authentication based on chaotic heartbeat waveform

Nomura

Umeda

et al. 2014

The 7th 2014 Biomedical Engineering International Conference

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In this paper, we propose a personal authentica tion method using heartbeat waveforms to enhance the security in wireless communication. In the heartbeat waveforms of a hu man being, it is known that there are discriminative characteris tic features and the mimicking is very difficult. Therefore, their application to personal authentication has been studied. Existing method performs personal authentication using a chaos indicator of ECG. In this paper, we propose an authentication method that extends the index in order to increase the accuracy of authentica tion. To validate that our personal authentication method is ap plicable, we perform some experiments to show that our method provides better authentication than existing one.

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“…Then, different features can be extracted from the ECG, giving rise to different classification techniques, that can be broadly distinguished into two main categories [7]: those based on fiducial points [6,9] or not [8,12,14] (e.g., using the Short Time Fourier Transform [16], Wavelet Transform [17], chaos extractor [18], Pulse Active Width [19]). The extracted features can be further processed to reduce their redundant information, e.g., by principal components analysis (PCA, [13]) or independent component analysis (ICA, [15]).…”

mentioning

confidence: 99%

A Low Cost ECG Biometry System Based on an Ensemble of Support Vector Machine Classifiers

Mesin

Múnera

Pasero

2016

Advances in Neural Networks

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The electrocardiogram (ECG) found many clinical applications. Recently, it was proposed as a promising technology also for biometric applications, i.e., to recognize a subject within a group of known people. For such an application, the accuracy of classical ECG clinical recordings is usually not needed, but the measurement procedure should be fast, robust and cheap. We developed an embedded wearable system for recording one-lead ECG from the wrists of a person. The system was used to record data from 10 subjects. Data were pre-processed to reduce the noise content. Then fiducial points were detected and used to train an ensemble of support vector machines to identify a person among the group. Mean classification accuracy was higher than 95 % if a single heartbeat was considered and higher than 98 % if 3 consecutive heartbeats were used, choosing by majority. The system is fast (a few seconds are needed), not invasive and can be used either standalone or together with other identification techniques to increase the safety level.

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Individual identification based on chaotic electrocardiogram signals

Cited by 26 publications

References 9 publications

ECG based biometric authentication using ensemble of features

ECG based biometric authentication using ensemble of features

Biometrics authentication based on chaotic heartbeat waveform

A Low Cost ECG Biometry System Based on an Ensemble of Support Vector Machine Classifiers

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