ECG-based identity recognition via deterministic learning

Dong, Xunde; Si, Wenjie; Huang, Weiyi

doi:10.1080/13102818.2018.1428500

Cited by 19 publications

(8 citation statements)

References 26 publications

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“…Remark 5: Compared to our previous works [32] and [45], the approach of this paper has the following advantages and improvements: (1) The method reduces the requirement for the number and location of ECG lead. The VX, VY, and VZ leads were used in [32], and the 12-lead ECG signal was used in [45], while the single-lead or double-lead ECG signal selected arbitrarily from the standard 12-lead ECG signal was used in this paper, making this method more applicable;…”

Section: Ggh Databasementioning

confidence: 96%

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Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

Dong

2020

IEEE Access

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Biometric identification based on electrocardiogram signals has attracted increasing attention. As the most dominant feature of the electrocardiogram signal, the QRS complex(i.e., the combination of ECG Q, R, and S waves) has been used for identification in some studies. This study aims to investigate the intra-individual stability of the QRS complex dynamics to assess its potential for human identification. The QRS complex dynamics are used as the unique feature to classify the QRS complex, which differs from the time/frequency domain features used in the literature. It is the fundamental feature of the QRS complex and contains the underlying information of the QRS complex. The dynamics of training QRS complexes are extracted and expressed as a constant radial basis function network by using deterministic learning. A set of estimators is constructed to represent the training QRS complexes using constant radial basis function networks. By comparing this set of estimators with the test QRS complex, a set of recognition errors is generated, and the average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training QRS complexes and that of the test QRS complex. Therefore, the test QRS complex can be recognized according to the smallest error principle. The electrocardiogram is classified according to the vote of the test QRS complexes recognition results. A private database and PTB diagnostic ECG database are used to test the proposed method. Experimental results on the private database (PTB database) showed that the average identification accuracy was 96.12% (97.42%) for 5-fold cross-validation based on one-lead electrocardiogram and 99.50% (99.23%) for 2-fold cross-validation based on two-lead electrocardiogram, respectively. These show that the dynamics of the QRS complex are well-differentiated for different individuals. INDEX TERMS Electrocardiogram, QRS complex, identity recognition, radial basis function networks, dynamics.

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Section: Ggh Databasementioning

confidence: 96%

“…(2) The method reduces the amount of computation. Both [32] and [45] performed identity recognition based on the entire ECG signal, whereas in this paper, identity recognition based only on the QRS complex of the ECG signal was performed;…”

Section: Ggh Databasementioning

confidence: 99%

Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

Dong

2020

IEEE Access

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“…The number of ECG beats within a short time is limited, and one beat is critical. Therefore, correcting one beat to eliminate heart rate effects is an important task, but most of studies have not considered compensating for ECG changes with various heart rates [15][16][17][18][19]. Among the studies considering some heart rate, many previous investigations resampled the entire ECG waveform by substituting the RR-interval into a linear equation.…”

Section: Introductionmentioning

confidence: 99%

ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions

Hwang

Kwon

Chung

et al. 2021

Sensors

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The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.

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“…These models did not consider the relationship between HFMD and potential impacting factors. With the development of artificial intelligence (AI), machine learning algorithms have shown their advantages in predictions and recognitions 21–23 . Gradient boosting tree (GBT) and random forest (RF) were found to be capable of identifying both mild and severe HFMD, which is helpful for early surveillance and control in HFMD 24,25 .…”

Section: Introductionmentioning

confidence: 99%

A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China

Liang

Song

et al. 2019

Sci Rep

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Hand-foot-mouth disease (HFMD) is a common infectious disease in children and is particularly severe in Guangxi, China. Meteorological conditions are known to play a pivotal role in the HFMD. Previous studies have reported numerous models to predict the incidence of HFMD. In this study, we proposed a new method for the HFMD prediction using GeoDetector and a Long Short-Term Memory neural network (LSTM). The daily meteorological factors and HFMD records in Guangxi during 2014–2015 were adopted. First, potential risk factors for the occurrence of HFMD were identified based on the GeoDetector. Then, region-specific prediction models were developed in 14 administrative regions of Guangxi, China using an optimized three-layer LSTM model. Prediction results (the R-square ranges from 0.39 to 0.71) showed that the model proposed in this study had a good performance in HFMD predictions. This model could provide support for the prevention and control of HFMD. Moreover, this model could also be extended to the time series prediction of other infectious diseases.

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ECG-based identity recognition via deterministic learning

Cited by 19 publications

References 26 publications

Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions

A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China

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