A Multiscale Autoregressive Model-Based Electrocardiogram Identification Method

Liu, Jikui; Yin, Liyan; He, Chenguang; Wen, Bo; Hong, Xia; Li, Ye

doi:10.1109/access.2018.2820684

Cited by 41 publications

(22 citation statements)

References 36 publications

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“…Feature engineering-based Multilayer Perceptrons (MLP) were previously used in ECG signal classification [32]. SVM was used as a classifier in [33] that obtained 93.15% accuracy for 50 subjects from the PTB dataset and another 140 subjects from a private dataset. SVM was also used to classify 10 subjects from the PTB dataset in [34], obtaining 97.45% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Alduwaile

Islam

2021

Entropy

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The electrocardiogram (ECG) signal has become a popular biometric modality due to characteristics that make it suitable for developing reliable authentication systems. However, the long segment of signal required for recognition is still one of the limitations of existing ECG biometric recognition methods and affects its acceptability as a biometric modality. This paper investigates how a short segment of an ECG signal can be effectively used for biometric recognition, using deep-learning techniques. A small convolutional neural network (CNN) is designed to achieve better generalization capability by entropy enhancement of a short segment of a heartbeat signal. Additionally, it investigates how various blind and feature-dependent segments with different lengths affect the performance of the recognition system. Experiments were carried out on two databases for performance evaluation that included single and multisession records. In addition, a comparison was made between the performance of the proposed classifier and four well-known CNN models: GoogLeNet, ResNet, MobileNet and EfficientNet. Using a time–frequency domain representation of a short segment of an ECG signal around the R-peak, the proposed model achieved an accuracy of 99.90% for PTB, 98.20% for the ECG-ID mixed-session, and 94.18% for ECG-ID multisession datasets. Using the preprinted ResNet, we obtained 97.28% accuracy for 0.5-second segments around the R-peaks for ECG-ID multisession datasets, outperforming existing methods. It was found that the time–frequency domain representation of a short segment of an ECG signal can be feasible for biometric recognition by achieving better accuracy and acceptability of this modality.

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Section: Related Workmentioning

confidence: 99%

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Alduwaile

Islam

2021

Entropy

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“…The extraction of features from ECG data can be classified into two categories: handcrafted and non-handcrafted. There are various handcrafted techniques for feature extraction such as using fiducial information [ 44 , 45 , 46 ], wavelet transform [ 40 , 47 , 48 , 49 ], and discrete cosine transform [ 42 , 50 ]. These approaches involve several processes such as feature normalization or removal of the noise designed by subjective decisions of the researchers.…”

Section: Background and Related Workmentioning

confidence: 99%

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Kim

Yang

Lee

et al. 2021

Sensors

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Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

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“…The identity vector is obtained by taking the average of the normalised context vectors, C n e , for all the templates corresponding to a subject. The expression to compute iECG is given in eq (16).…”

Section: E Verification Modementioning

confidence: 99%

“…C n e C n e (16) During verification, the cosine similarity value between the claimant iECG and the claimed iECG is computed. Finally, the authenticity is verified by comparing it with a pre-set threshold value.…”

Section: E Verification Modementioning

confidence: 99%

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

Jyotishi¹,

Dandapat²

2021

Preprint

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The electrocardiogram (ECG) based biometric sys- tem has recently gained popularity. Easy signal acquisition and robustness against falsification are the major advantages of the ECG based biometric system. This biometric system can help automate the subject identification and authentication aspect of personalised healthcare services. In this paper, we have designed a novel attention based hierarchical long short-term memory (LSTM) model to learn the biometric representation correspond- ing to a person. The hierarchical LSTM model proposed in this paper can learn the temporal variation of the ECG signal in different abstractions. This addresses the long term dependency issue of the LSTM network in our application. The attention mechanism of the model learns to capture the ECG complexes that have more biometric information corresponding to each person. These ECG complexes are given more weight to learn better biometric representation. The proposed system is less complex and more efficient as it does not require the detection of any fiducial points. We have evaluated the proposed model for both the person verification and identification problems using two on-the-person ECG databases and two off-the-person ECG databases. The proposed framework is found to perform better than the existing fiducial and non-fiducial point based methods.

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A Multiscale Autoregressive Model-Based Electrocardiogram Identification Method

Cited by 41 publications

References 36 publications

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Using Convolutional Neural Network and a Single Heartbeat for ECG Biometric Recognition

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

An Attention Based Hierarchical LSTM Architecture for ECG Biometric System

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