ECG biometric recognition using SVM‐based approach

Rezgui, Dhouha; Lachiri, Zied

doi:10.1002/tee.22241

Cited by 32 publications

(15 citation statements)

References 20 publications

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“…The proposed combination of Savitzky-Golay with a moving average filter (SG + MAF) had its performance compared with the most successful prior art methods: bandpass filters (BPF) with bands 1–40 Hz [18,25,26], 2–40 Hz [49,61], 1–30 Hz [27], and 2–30 Hz [16,29]; Savitzky-Golay (SG) [36]; Discrete Cosine Transform (DCT) [42]; and the combinations of Discrete Wavelet Transform with a moving average filter (DWT + MAF) [55] and with a highpass filter (DWT + HPF) [40]. …”

Section: Resultsmentioning

confidence: 99%

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Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Pinto

Cardoso

Lourenço

et al. 2017

Sensors

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Electrocardiogram signals acquired through a steering wheel could be the key to seamless, highly comfortable, and continuous human recognition in driving settings. This paper focuses on the enhancement of the unprecedented lesser quality of such signals, through the combination of Savitzky-Golay and moving average filters, followed by outlier detection and removal based on normalised cross-correlation and clustering, which was able to render ensemble heartbeats of significantly higher quality. Discrete Cosine Transform (DCT) and Haar transform features were extracted and fed to decision methods based on Support Vector Machines (SVM), k-Nearest Neighbours (kNN), Multilayer Perceptrons (MLP), and Gaussian Mixture Models - Universal Background Models (GMM-UBM) classifiers, for both identification and authentication tasks. Additional techniques of user-tuned authentication and past score weighting were also studied. The method’s performance was comparable to some of the best recent state-of-the-art methods (94.9% identification rate (IDR) and 2.66% authentication equal error rate (EER)), despite lesser results with scarce train data (70.9% IDR and 11.8% EER). It was concluded that the method was suitable for biometric recognition with driving electrocardiogram signals, and could, with future developments, be used on a continuous system in seamless and highly noisy settings.

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Section: Resultsmentioning

confidence: 99%

“…Towards this goal, fiducial and time domain features have been widely chosen on the prior art [29,45,50,60,61]. …”

Section: Proposed Methodologymentioning

confidence: 99%

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Pinto

Cardoso

Lourenço

et al. 2017

Sensors

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“…Generally, the goodness of a heartbeat based method in short-term ECG signal identification is mainly measured by the single heartbeat identification accuracy. Our method gave higher heartbeat identification accuracy of 89.41% on signals than the other five methods, which suggested that our method could provide an efficient way for short-term ECG identification [40,41].…”

Section: Methodsmentioning

confidence: 75%

“…Then the heartbeat was processed by the proposed QRS-centered resampling strategy and standardized to 400 sampling points. The QRS-centered strategy is inspired and based on the prior ECG identification works: Firstly, to our knowledge, all the existing literature about ECG identification has taken QRS complex or its related form as features and QRS complex is very important for identifying a person [9,10,12,18,[40][41][42][43][44][45][46]. To preserve all potential identity information of the QRS complex, we use a length-fixed window to keep the QRS complex original.…”

Section: Discussionmentioning

confidence: 99%

A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

Wang

Yang

et al. 2019

Applied Sciences

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In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

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“…However, a choice of an optimal wavelet is still challenging [18] and the approach has low efficiency in smoothing ECG signals. Other algorithms tested for such needs include the principal component analysis (PCA) [19], linear discriminant analysis (LDA) [20], independent component analysis (ICA) [21], support vector machine [22], and neural networks [23].…”

Section: Introductionmentioning

confidence: 99%

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

Lastre-Dominguez

Shmaliy

Ibarra-Manzano

et al. 2019

BioMed Research International

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Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p<0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

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ECG biometric recognition using SVM‐based approach

Cited by 32 publications

References 20 publications

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel

A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing

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