The Morphology of the Electrocardiogram for Eevaluating ECG Biometrics

Tsao, Ya-Ting; Shen, Tsu-Wang; Ko, Tung-Fu; Lin, Tsung-Hsing

doi:10.1109/health.2007.381637

Cited by 18 publications

(11 citation statements)

References 3 publications

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“…Although Tsao et al [75] showed that, in theory, it is possible to fool an ECG biometric system by synthesizing an ECG recording using measured features, in practice it may be difficult to replicate an ECG signal at the sensor level.…”

Section: Discussionmentioning

confidence: 99%

ECG Biometric Recognition: A Comparative Analysis

Odinaka

Lai

Kaplan

et al. 2012

IEEE Trans.Inform.Forensic Secur.

306

196

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The electrocardiogram (ECG) is an emerging biometric modality that has seen about thirteen years of development, in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our in-house database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG based biometrics perform well, when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 2 equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

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Section: Discussionmentioning

confidence: 99%

ECG Biometric Recognition: A Comparative Analysis

Odinaka

Lai

Kaplan

et al. 2012

IEEE Trans.Inform.Forensic Secur.

306

196

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show abstract

“…Shen et al [10] extracted seven temporal and amplitude features from the QRST wave. These are then introduced to decision based neural network (DBNN) as an input and experiment results shows a of gain 80 % accuracy within the population of 20 people.…”

Section: Related Workmentioning

confidence: 99%

“…In theory, it is possible to compromise or bypass an ECG biometric system, by synthesizing an ECG recording using measured features. However, in practice, it is extremely difficult to replicate an ECG signal at the sensor level [10].…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

TuerxunWaili

Nor

Rahman

et al. 2016

Recent Advances in Information and Communication Technology 2016

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This paper presents a Multilayer Perception Neural Network developed to identify human subjects using electrocardiogram (ECG) signals. We use the amplitude values of Q, R and S as a features for our experiments. In this study, a total of 87 dataset were collected among 14 subjects from the Physikalisch-Technische Bundesanstalt (PTB) database. Out of the 14 subjects, Q-R-S feature points were taken from different day and time sessions to perform classification with MLP. Out of this data, 66 % is used as training dataset while the remaining 34 % is used for testing. Our method yields 96 % accuracy and demonstrates that the use of three fiducial points is sufficient to identify a subject despite the common practice of taking more feature points.

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“…It distinguishes itself by being a liveliness indicator and a difficult to falsify biometric modality. The study of [33] showed that the ECG biometric system can be fooled by synthesizing an ECG recording using measured features. But practically it may be harder to replicate an ECG signal at the acquisition level and fool the ECG sensors.…”

Section: Resultsmentioning

confidence: 99%

Individual Identification Using Linear Projection of Heartbeat Features

Singh

2014

Applied Computational Intelligence and Soft Computing

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This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively.

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The Morphology of the Electrocardiogram for Eevaluating ECG Biometrics

Cited by 18 publications

References 3 publications

ECG Biometric Recognition: A Comparative Analysis

ECG Biometric Recognition: A Comparative Analysis

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

Individual Identification Using Linear Projection of Heartbeat Features

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