ECG biometric identification for general population using multiresolution analysis of DWT based features

Dar, Muhammad Najam; Akram, M. Usman; Usman, Anam; Khan, Shoab Ahmed

doi:10.1109/infosec.2015.7435498

Cited by 36 publications

(30 citation statements)

References 15 publications

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“…We found various conventional classification methods being used on NSRDB and MITDB datasets. For the NSRDB dataset, the reported classification accuracy ranged from 99.4% to 100% [45,46], while for the MITDB dataset, the reported accuracy ranged from 93.1% to 100% [15,19,26,[46][47][48][49][50]. The RNN-based method outperforms the aforementioned methods on both datasets.…”

Section: Resultsmentioning

confidence: 95%

ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks

Kim

Pyun

2020

Sensors

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Securing personal authentication is an important study in the field of security. Particularly, fingerprinting and face recognition have been used for personal authentication. However, these systems suffer from certain issues, such as fingerprinting forgery, or environmental obstacles. To address forgery or spoofing identification problems, various approaches have been considered, including electrocardiogram (ECG). For ECG identification, linear discriminant analysis (LDA), support vector machine (SVM), principal component analysis (PCA), deep recurrent neural network (DRNN), and recurrent neural network (RNN) have been conventionally used. Certain studies have shown that the RNN model yields the best performance in ECG identification as compared with the other models. However, these methods require a lengthy input signal for high accuracy. Thus, these methods may not be applied to a real-time system. In this study, we propose using bidirectional long short-term memory (LSTM)-based deep recurrent neural networks (DRNN) through late-fusion to develop a real-time system for ECG-based biometrics identification and classification. We suggest a preprocessing procedure for the quick identification and noise reduction, such as a derivative filter, moving average filter, and normalization. We experimentally evaluated the proposed method using two public datasets: MIT-BIH Normal Sinus Rhythm (NSRDB) and MIT-BIH Arrhythmia (MITDB). The proposed LSTM-based DRNN model shows that in NSRDB, the overall precision was 100%, recall was 100%, accuracy was 100%, and F1-score was 1. For MITDB, the overall precision was 99.8%, recall was 99.8%, accuracy was 99.8%, and F1-score was 0.99. Our experiments demonstrate that the proposed model achieves an overall higher classification accuracy and efficiency compared with the conventional LSTM approach.

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Section: Resultsmentioning

confidence: 95%

ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks

Kim

Pyun

2020

Sensors

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“…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%

“…The literature [9] presented a recurrent neural network (RNN)-based method, which could achieve automatic feature extraction, to improve the identification performance on ECG signals from both the same session and different sessions. Discrete wavelet transform was used to extract wavelet coefficients as the feature vector and KNN was applied as the classifier in literature [10]. A novel automatic ECG identification approach combining back propagation neural network (BP-NN) with Frequency Rank Order Statistics (FROS) was introduced to distinguish different subjects in the literature [11].…”

Section: Introductionmentioning

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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“…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%

“…The classifier is the last stage of a biometric identification system. Different classifiers have been used in the literature such as neural network (NN), k-nearest neighbors algorithm (k-NN), support vector machine (SVM), and random forest [30,31,33,49,[54][55][56]. Recently, deep learning has also been proposed for an ECG biometric identification system [57,58].…”

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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ECG biometric identification for general population using multiresolution analysis of DWT based features

Cited by 36 publications

References 15 publications

ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks

ECG Identification For Personal Authentication Using LSTM-Based Deep Recurrent Neural Networks

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

ECG-Based Subject Identification Using Statistical Features and Random Forest

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