A novel feature extraction method in ECG biometrics

Hamdi, Takoua; Slimane, Anis Ben; Khalifa, Anouar Ben

doi:10.1109/ipas.2014.7043304

Cited by 19 publications

(6 citation statements)

References 12 publications

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“…Furthermore, mean, standard deviation, range, interquartile range (IQR), percentiles of energy, slope and angles of P-QRS-T waves [13], PR intervals, and R amplitudes are extracted.…”

Section: Feature Engineeringmentioning

confidence: 99%

Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier

Zabihi

Rad

Katsaggelos

et al. 2017

Computing in Cardiology Conference (CinC)

116

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“…Furthermore, mean, standard deviation, range, interquartile range (IQR), percentiles of energy, slope and angles of P-QRS-T waves [13], PR intervals, and R amplitudes are extracted.…”

Section: Feature Engineeringmentioning

confidence: 99%

Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier

Zabihi

Rad

Katsaggelos

et al. 2017

Computing in Cardiology Conference (CinC)

116

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“…Furthermore, MLP has better generalization ability on noisy data, [14]. Research on ECG Biometric started with the pioneering work of Biel et al [15] who purposed the use of amplitude, distance of waves as features to identify a subject. He also proves that a single channel ECG contains enough discriminant information for recognition.…”

Section: Related Workmentioning

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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“…MA provided the best result (97.93%). Teodoro et al worked with more users and produced a more acceptable result than the previous works where accuracy even moved up to 100% (PTB) [2] or 96.44% (Private DB) [3] but with less users.…”

Section: Literature Reviewmentioning

confidence: 94%

Runtime Optimization of Identification Event in ECG Based Biometric Authentication

Neehal

Karim

Banik

et al. 2019

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)

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Biometric Authentication has become a very popular method for different state-of-the-art security architectures. Albeit the ubiquitous acceptance and constant development in trivial biometric authentication methods such as fingerprint, palm-print, retinal scan etc., the possibility of producing highly competitive performance from somewhat less-popular methods still remains. Electrocardiogram (ECG) based biometric authentication is such a method, which, despite its limited appearance in earlier research works, are currently being observed as equivalently high-performing as other trivial popular methods. In this paper, we have proposed a model to optimize the runtime of identification event in ECG based biometric authentication and we have achieved a maximum of 79.26% time reduction with 100% accuracy.Where w1 = 0.2, w2 = 0.5 and w3 = 0.3 was chosen as optimal weight values via trial and error.

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A novel feature extraction method in ECG biometrics

Cited by 19 publications

References 12 publications

Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier

Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier

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

Runtime Optimization of Identification Event in ECG Based Biometric Authentication

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