A Novel Personal Identity Verification Approach Using a Discrete Wavelet Transform of the ECG Signal

Chiu, Chuang-Chien; Chuang, Chou-Min; Hsu, Chih-Yu

doi:10.1109/mue.2008.67

Cited by 78 publications

(57 citation statements)

References 8 publications

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“…This section provides a detailed review of research geared toward the usefulness of ECGs as biometric indicators. Since the first such effort was reported in 1999 (Biel, et al, 1999), approximately 20 different groups have researched this interesting area (Israel, et al, 2005;Chan, et al, 2008;Biel, et al, 2001;Zhang & Wei, 2006;Biel, et al, 1999;Boumbarov, et al, 2009;Chan, et al, 2006;Chiu, et al, 2008;Fatemian & Hatzinakos, 2009;Gahi, et al, 2008;Israel, et al, 2003;Kim, et al, 2005;Kyoso, 2003;Kyoso & Uchiyama, 2001;Micheli-Tzanakou, et al, 2009;Nasri, et al, 2009;Plataniotis, et al, 2006;Shen, et al, 2002;Sufi & Khalil, 2008;Wang, et al, 2006;Yao & Wan, 2010) -see Table 1. Most of these published works use a similar approach to present research findings.…”

Section: Ecg As a Biometric Modality: A Systematic Reviewmentioning

confidence: 99%

“…• Data Collection: Some publications provide subject demographic data, including gender (Biel, et al, 2001;Kim, et al, 2005;, age range (Biel, et al, 2001;Chan, et al, 2008;Kim, et al, 2005;Yao & Wan, 2010), and heart condition (Agrafioti & Hatzinakos, 2008a;Chiu, et al, 2008;Kim, et al, 2005;Plataniotis, et al, 2006;Singh & Gupta, 2008;Sufi & Khalil, 2008). However, few report complete demographic or health-condition information for participants.…”

Section: Ecg As a Biometric Modality: A Systematic Reviewmentioning

confidence: 99%

“…However, few report complete demographic or health-condition information for participants. Furthermore, the time interval between subject enrollment and data collection, a critical element when determining the effectiveness of a biometric modality, is frequently overlooked (Chiu, et al, 2008;Gahi, et al, 2008;Israel, et al, 2005;Kim, et al, 2005;Kyoso, 2003;Plataniotis, et al, 2006;Saechia, et al, 2005;Sufi & Khalil, 2008). Even studies that record this information often mention it vaguely (Chan, et al, 2008;Fatemian & Hatzinakos, 2009;Singh & Gupta, 2008) (see Table 1).…”

Section: Ecg As a Biometric Modality: A Systematic Reviewmentioning

confidence: 99%

“…Others believe that post-transform features are more distinctive and will therefore improve identification performance. For example, wavelet transformation was used in (Chan, et al, 2006;Chan, et al, 2008;Chiu, et al, 2008; to find the wavelet coefficients and distances in the wavelet domain that optimally quantify the similarity between two ECGs. Autocorrelation coefficients are a third type of statistical feature under investigation (Agrafioti & Hatzinakos, 2008a;Plataniotis, et al, 2006).…”

Section: Ecg As a Biometric Modality: A Systematic Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Biometric Applications of One-Dimensional Physiological Signals - Electrocardiograms

Yao¹,

Wan²,

Warren³

2011

Recent Application in Biometrics

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Section: Ecg As a Biometric Modality: A Systematic Reviewmentioning

confidence: 99%

Section: Ecg As a Biometric Modality: A Systematic Reviewmentioning

confidence: 99%

Section: Ecg As a Biometric Modality: A Systematic Reviewmentioning

confidence: 99%

Section: Ecg As a Biometric Modality: A Systematic Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Biometric Applications of One-Dimensional Physiological Signals - Electrocardiograms

Yao¹,

Wan²,

Warren³

2011

Recent Application in Biometrics

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“…The former describes methods based on reference points in the signals (Israel et al, 2005;Shen et al, 2002;Silva et al, 2007b;Oliveira and Fred, 2009), while the latter methods rely on intrinsic information within the ECG signal, without having any particular cues as reference (Chan et al, 2008;Chiu et al, 2008;Wang et al, 2008;Coutinho et al, 2013). Partially fidu- cial methods, like our approach presented below, rely on fiducial information only for ECG segmentation (Wang et al, 2008;Lourenço et al, 2011;Carreiras et al, 2013a;Silva et al, 2013c).…”

Section: Introductionmentioning

confidence: 99%

ECG Signals for Biometric Applications - Are we there yet?

Carreiras

Lourenço

Fred

et al. 2014

Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics

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Abstract:The potential of the electrocardiographic (ECG) signal as a biometric trait has been ascertained in the literature over the past decade. The inherent characteristics of the ECG make it an interesting biometric modality, given its universality, intrinsic aliveness detection, continuous availability, and inbuilt hidden nature. These properties enable the development of novel applications, where non-intrusive and continuous authentication are critical factors. Examples include, among others, electronic trading platforms, the gaming industry, and the auto industry, in particular for car sharing programs and fleet management solutions. However, there are still some challenges to overcome in order to make the ECG a widely accepted biometric. In particular, the questions of uniqueness (inter-subject variability) and permanence over time (intra-subject variability) are still largely unanswered. In this paper we focus on the uniqueness question, presenting a preliminary study of our biometric recognition system, testing it on a database encompassing 618 subjects. We also performed tests with subsets of this population. The results reinforce that the ECG is a viable trait for biometrics, having obtained an Equal Error Rate of 9.01% and an Error of Identification of 15.64% for the entire test population.

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ECG biometric recognition using SVM‐based approach

Rezgui

Lachiri

2016

IEEJ Transactions Elec Engng

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This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twentyone features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT-BIH Arrhythmia and MIT-BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia.

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A Novel Personal Identity Verification Approach Using a Discrete Wavelet Transform of the ECG Signal

Cited by 78 publications

References 8 publications

Biometric Applications of One-Dimensional Physiological Signals - Electrocardiograms

Biometric Applications of One-Dimensional Physiological Signals - Electrocardiograms

ECG Signals for Biometric Applications - Are we there yet?

ECG biometric recognition using SVM‐based approach

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