Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space

Fang, Shih-Chin; Chan, Hsiao‐Lung

doi:10.1016/j.patcog.2008.11.020

Cited by 125 publications

(77 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since [1], a set of ECG-based biometric studies has occurred in the literature proposing solutions for identification [1,[5][6][7][8][9][10][11][12][13][14][15], or verification [16][17][18] or both [19][20][21][22]. A set of them, in particular the first studies, proposed systems based on the extraction of a set of fiducial temporal and amplitude features from the ECG, from the P-QRS-T [1, 5-7, 10, 11, 16] or only the QRS-T segment [8,23], which is generally a difficult task.…”

Section: Introductionmentioning

confidence: 99%

“…A set of them, in particular the first studies, proposed systems based on the extraction of a set of fiducial temporal and amplitude features from the ECG, from the P-QRS-T [1, 5-7, 10, 11, 16] or only the QRS-T segment [8,23], which is generally a difficult task. To bypass it, more recent approaches compute non fiducial parameters between windowed ECG into single heartbeat signals, needing only the R-peaks detection [13,19,21,22], except in [9,12] where no waveform detection is required. When the ECG is segmented, the template matching is performed between windows of various lengths: 100 ms in [19], 120 ms and 600 ms in [13], 700 ms in [21] and 750 ms in [22].…”

Section: Introductionmentioning

confidence: 99%

“…Even if it represents an interest from a technological point of view, in [14] the number of leads is equal to 2, in [19] it is equal to 3, in [23] it is equal to 12 and different numbers of leads are compared in [13] (1 and 3), in [12] (1 and 12) and in [1] (1, 6 and 12).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ECG biometric analysis in different physiological recording conditions

Porée

Kervio

Carrault

2014

SIViP

View full text Add to dashboard Cite

Biometric systems have for objective to perform identification, or verification of identity of individuals. Human electrocardiogram (ECG) has been recently proposed as an additional tool for biometric applications. Then, a set of ECG-based biometric studies has occurred in the literature but they are difficult to compare because they use various values of: the number of ECG leads, the length of the analysis window (only the QRS or more), the delays between recordings... However, they analyze nearly always the ECG in rest conditions. Here, we propose to evaluate the possibility of performing ECG-based biometry in other conditions. For this purpose, a comparative study, on three experimental conditions (supine rest, standing and exercise), has been carried out. It is based on the computing of the correlation coefficient between pairs of shapes of windowed ECG. Both verification and identification tasks are tackled. The results show that there is no advantage in comparing shapes recorded in supine rest conditions, as it is classically done, which represents an obvious benefit in biometry. Performances are evaluated as a function of the shape length. Then, different tests are performed in order to investigate how should be constructed the enrolment database when a system is devoted to work in several conditions. Last part of the paper shows how performances depend on the time and on the number of ECG leads.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ECG biometric analysis in different physiological recording conditions

Porée

Kervio

Carrault

2014

SIViP

View full text Add to dashboard Cite

show abstract

“…In the preprocessing step, noise is removed by a bandpass Butterworth filter [11,30] of order four with cut-off frequencies of 0.25 to 40 Hz.…”

Section: Preprocessingmentioning

confidence: 99%

Heartbeat Biometrics for Remote Authentication Using Sensor Embedded Computing Devices

Islam

2015

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Biometric verification has been included in remote user authentication schemes recently. In this paper, we have proposed the use of heartbeat biometrics for its liveness property as a possible defense against spoof attacks in remote authentication. Sensor embedded mobile computing devices such as smart phones could be used to capture biometric signals and to replace the use of traditional smart cards. For the remote authentication, we have adopted the state-of-the-art scheme. We have described the biometric verification method instead of using cryptographic hash function for feature matching. We have used the piecewise aligned morphology (PAM) of heartbeat signal as a biometric feature. The biometric verification method was tested with ECG records of 100 individuals and 1.34% of equal error rate (EER) was obtained. This feature could be computed and transmitted efficiently using mobile computing devices. The proposed scheme offers more security and convenience to users. Additionally, it becomes robust against noisy biometric input. The proposed scheme should be feasible for remote authentication using sensor embedded computing devices in many practical applications such as remote healthcare.

show abstract

“…The features used in the literature include fiducial features (e.g. QRS duration, T wave amplitude) derived from characteristic points of ECG signals [7,8,10,11], non-fiducial features derived from segmented windows of ECG signals [13,14] and hybrid features [3,15]. However, these static features cannot characterize ECG signals adequately, since ECG signals are essentially temporal patterns (i.e.…”

Section: Introductionmentioning

confidence: 99%

ECG-based identity recognition via deterministic learning

Dong

Huang

2018

Biotechnology & Biotechnological Equipment

View full text Add to dashboard Cite

In this paper, a novel method based on electrocardiogram (ECG) signals is proposed for identity recognition. A unique feature called dynamics, which is fundamentally different from features used in literature, is extracted from ECG signals and used for identity recognition. Deterministic learning, a recently proposed machine learning approach, is used to model the dynamics of training ECG signals. A set of estimators employing the modelling results of training ECG signals is constructed. Through comparing the test ECG signal (measured from a subject to be recognized) with the estimators, a set of errors can be obtained and used to measure the similarity between the test and the training ECG signals. The test ECG signal is recognized in accordance with the smallest error, and then the subject can be recognized rapidly. Experimental results indicate that the proposed method is reliable and efficient for identity recognition.

show abstract

Human identification by quantifying similarity and dissimilarity in electrocardiogram phase space

Cited by 125 publications

References 4 publications

ECG biometric analysis in different physiological recording conditions

ECG biometric analysis in different physiological recording conditions

Heartbeat Biometrics for Remote Authentication Using Sensor Embedded Computing Devices

ECG-based identity recognition via deterministic learning

Contact Info

Product

Resources

About