Identifying Individuals Using Fourier and Discriminant Analysis of Electrocardiogram

Srivastva, Ranjeet; Singh, Yatindra Nath

doi:10.1007/978-981-13-0023-3_27

Cited by 6 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The long‐term stability of ECG features is verified by researchers in [9] and [10]. The uniqueness of ECG features has already been proved in our studies of [6, 7, 38]. Hence, the proposed biometric system meets all requirements of an automatic biometric system such as universality, uniqueness, long‐term stability, and difficulty to circumvention.…”

Section: Resultssupporting

confidence: 55%

“…In order to manifest the distinctness of ECG features in a better way, the feature vectors formed by AC coefficients are transformed using the transformation techniques such as discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh–Hadamard transform (WHT). The dimensionality of transformed feature vectors is reduced by applying the techniques employed linear combinations of features such as principle component analysis (PCA) [6] and linear discriminant analysis (LDA) [7]. The LDA proved to be better among all three transformations on the experimented MIT‐BIH Arrhythmia database and QT database of Physionet [42].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ECG analysis for human recognition using non‐fiducial methods

Srivastva

Singh

2019

IET biom.

Self Cite

View full text Add to dashboard Cite

The electrocardiogram (ECG) has emerged as a new biometric for human recognition due to its robustness against fraudulent attacks. This article presents a novel method of ECG biometric for human recognition using autocorrelation (AC) followed by one of the three transformation techniques, i.e. discrete cosine transform (DCT), discrete Fourier transform (DFT), and Walsh-Hadamard transform (WHT). The effectiveness of these transformations is evaluated on the dimensionality reduction techniques i.e. principal component analysis and linear discriminant analysis (LDA). Thus, the systems prepared by different combinations of transformations and dimensionality reduction techniques are evaluated on publically available databases of Physionet. The authentication and identification accuracies achieved by these systems are found the best on DFT and LDA combination. The authentication performance is reported to 99.98% (99.83%), whereas the average rank classification accuracy is reported to 100% (97%) on QT database (MIT-BIH arrhythmia database) of Physionet.

show abstract

Section: Resultssupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

ECG analysis for human recognition using non‐fiducial methods

Srivastva

Singh

2019

IET biom.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, ECG is often attributed to provide information about liveness of a person [21,25,30,58,64,67]. However, we do not share the view that ECG inherently provides secure liveness detection, since related work has demonstrated a successful attack [19]: Using a waveform generator, the authors tricked an ECG sensor and biometrics system into authenticating despite not being attached to a living human.…”

Section: Practical Use Of Ecg Among Other Biometricsmentioning

confidence: 96%

Heartbeats in the Wild: A Field Study Exploring ECG Biometrics in Everyday Life

Lehmann

Buschek

2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

This paper reports on an in-depth study of electrocardiogram (ECG) biometrics in everyday life. We collected ECG data from 20 people over a week, using a non-medical chest tracker. We evaluated user identification accuracy in several scenarios and observed equal error rates of 9.15% to 21.91%, heavily depending on 1) the number of days used for training, and 2) the number of heartbeats used per identification decision. We conclude that ECG biometrics can work in the wild but are less robust than expected based on the literature, highlighting that previous lab studies obtained highly optimistic results with regard to real life deployments. We explain this with noise due to changing body postures and states as well as interrupted measures. We conclude with implications for future research and the design of ECG biometrics systems for real world deployments, including critical reflections on privacy.

show abstract

ECG Biometric Human Identification using Convolutional Neural Network

2024

2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

View full text Add to dashboard Cite

Identifying Individuals Using Fourier and Discriminant Analysis of Electrocardiogram

Cited by 6 publications

References 18 publications

ECG analysis for human recognition using non‐fiducial methods

ECG analysis for human recognition using non‐fiducial methods

Heartbeats in the Wild: A Field Study Exploring ECG Biometrics in Everyday Life

ECG Biometric Human Identification using Convolutional Neural Network

Contact Info

Product

Resources

About