QRS detection-free electrocardiogram biometrics in the reconstructed phase space

Fang, Shih-Chin; Chan, Hsiao‐Lung

doi:10.1016/j.patrec.2012.11.005

Cited by 36 publications

(27 citation statements)

References 11 publications

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“…Анализ фазового пространства. Фанг установил, что анализ фазовой траектории нормализованных по амплитуде ЭКГ с задержкой (4-36 мс) позволяет выявлять ранее не известные биометрические признаки [9].…”

Section: Statistical Assessment Of Biometrical Signs By Electrocardiounclassified

Statistical Assessment of Biometrical Signs by Electrocardiography

Bogdanov¹,

Kartak²,

Dumchikov³

et al. 2018

Russ J Cardiol

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Statistical analysis performed, of the informativity of biometric signs of electrocardiogram. It was found that amplitude and time parameters of PQST areas of electrocardiograms show enough dispersion and are not easily distinguished. For reliable biometric personality identification quite a range of such signs is needed. Based on the known signs, novel signs were formulated via the bootstrap method. The novel signs presented much lower dispersion. It was found, that reliable identification of personality is possible with combined usage of the amplitudes in Sand T-areas of cardiocycle.

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Section: Statistical Assessment Of Biometrical Signs By Electrocardiounclassified

Statistical Assessment of Biometrical Signs by Electrocardiography

Bogdanov¹,

Kartak²,

Dumchikov³

et al. 2018

Russ J Cardiol

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show abstract

“…Recently, Fang and Chan [28] implemented a QRS detection-free ECG biometric based on the phase space trajectory of the ECG signal using a proposed coarse grained-structure. They performed tests using 100 subjects with achieved accuracy of up to 99% when using three-leads ECG.…”

Section: Prior Workmentioning

confidence: 99%

“…No features from the cardiac function were extracted, but only their waveforms were compared. Fang and Chan [28,31] proposed a transformation of the ECG signal, derived from one and three lead ECGs into a three-dimensional space.…”

Section: Prior Workmentioning

confidence: 99%

A novel biometric authentication approach using ECG and EMG signals

Belgacem

Fournier

Naït‐Ali

et al. 2015

Journal of Medical Engineering & Technology

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Security biometrics is a secure alternative to traditional methods of identity verification of individuals, such as authentication systems based on user name and password. Recently, it has been found that the electrocardiogram (ECG) signal formed by five successive waves (P, Q, R, S and T) is unique to each individual. In fact, better than any other biometrics' measures, it delivers proof of subject's being alive as extra information which other biometrics cannot deliver. The main purpose of this work is to present a low-cost method for online acquisition and processing of ECG signals for person authentication and to study the possibility of providing additional information and retrieve personal data from an electrocardiogram signal to yield a reliable decision. This study explores the effectiveness of a novel biometric system resulting from the fusion of information and knowledge provided by ECG and EMG (Electromyogram) physiological recordings. It is shown that biometrics based on these ECG/EMG signals offers a novel way to robustly authenticate subjects. Five ECG databases (MIT-BIH, ST-T, NSR, PTB and ECG-ID) and several ECG signals collected in-house from volunteers were exploited. A palm-based ECG biometric system was developed where the signals are collected from the palm of the subject through a minimally intrusive one-lead ECG set-up. A total of 3750 ECG beats were used in this work. Feature extraction was performed on ECG signals using Fourier descriptors (spectral coefficients). Optimum-Path Forest classifier was used to calculate the degree of similarity between individuals. The obtained results from the proposed approach look promising for individuals' authentication.

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“…Feature extraction is needed to provide unique biomarkers for a given ECG signal. Feature extraction methods can be grouped into three main categories: fiducial-based approaches which extract features while preserving the characteristics of the ECG signal, e.g., the amplitudes and intervals of heartbeats [20,31,[37][38][39][40][41][42][43], nonfiducial-based approaches which do not require such precise knowledge of ECG characteristics [44][45][46][47][48][49][50][51][52][53], and hybrid-based approaches [54,55].…”

Section: Introductionmentioning

confidence: 99%

ECG-Based Subject Identification Using Statistical Features and Random Forest

et al. 2019

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In this work, a nonfiducial electrocardiogram (ECG) identification algorithm based on statistical features and random forest classifier is presented. Two feature extraction approaches are investigated: direct and band-based approaches. In the former, eleven simple statistical features are directly extracted from a single-lead ECG signal segment. In the latter, the single-lead ECG signal is first decomposed into bands, and the statistical features are extracted from each segment of a given band and concatenated to form the feature vector. Nonoverlapping segments of different lengths (i.e., 1, 3, 5, 7, 10, or 15 sec) are examined. The extracted feature vectors are applied to a random forest classifier, for the purpose of identification. This study considers 290 reference subjects from the ECG database of the Physikalisch-Technische Bundesanstalt (PTB). The proposed identification algorithm achieved an accuracy rate of 99.61% utilizing the single limb lead (I) with the band-based approach. A single chest lead (V1), augmented limb lead (aVF), and Frank’s lead (Vx) achieved an accuracy rate of 99.37%, 99.76%, and 99.76%, respectively, using the same approach.

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QRS detection-free electrocardiogram biometrics in the reconstructed phase space

Cited by 36 publications

References 11 publications

Statistical Assessment of Biometrical Signs by Electrocardiography

Statistical Assessment of Biometrical Signs by Electrocardiography

A novel biometric authentication approach using ECG and EMG signals

ECG-Based Subject Identification Using Statistical Features and Random Forest

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