An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning

Alkeem, Ebrahim Al; Kim, Song-Kyoo; Yeun, Chan Yeob; Zemerly, Mohamed Jamal; Poon, Kin Fai; Gianini, G.; Yoo, Paul D.

doi:10.1109/access.2019.2937357

Cited by 72 publications

(34 citation statements)

References 30 publications

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“…Step 4: A set of state errors can be obtained according to the error system as (12), compute the average L 1 norm of the state estimation error;…”

Section: ) Mechanism For Subject Identity Recognitionmentioning

confidence: 99%

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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“…Step 4: A set of state errors can be obtained according to the error system as (12), compute the average L 1 norm of the state estimation error;…”

Section: ) Mechanism For Subject Identity Recognitionmentioning

confidence: 99%

Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

Dong

2020

IEEE Access

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“…In terms of our mutual information theoretic feature selection, we used random variables which are represented as features and class labels [29]. We applied a widely-used histogram-based approach to estimate the probability distribution [16]. This ignores the potential existence of more optimal subsets, comprising features that are not sequentially ranked in terms of their mutual information values.…”

Section: A Security Check Case: Experimental Setupsmentioning

confidence: 99%

“…The detailed process flow for the training and testing phases is summarized in Fig. 3, which has been adapted from our previous research [16]. The training phase generates the reference regression functions for each entity using the DT technique and stores all functions as a database.…”

Section: A Security Check Case: Experimental Setupsmentioning

confidence: 99%

“…ECG data can be sliced in the time domain to provide a signature that is unique to each individual [16][17][18][19] and time slicing with R-peak anchoring is highly effective for ECG analysis [16]. In this approach, only the R-peaks are used without detecting other ECG waveforms [16]. Slicing the ECG data based on the interval between heartbeats (the RR-interval) instead of the slicing window time is the basis of our proposed RR-interval framing (RRIF) method, wherein each sliced data sample is used as an input parameter for ML training.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Machine Learning-Based Biometric Authentication System Using RR-Interval Framed Electrocardiograms

2019

Self Cite

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This paper is targeted in the area of biometric data enabled security system based on the machine learning for the digital health. The disadvantages of traditional authentication systems include the risks of forgetfulness, loss, and theft. Biometric authentication is therefore rapidly replacing traditional authentication methods and is becoming an everyday part of life. The electrocardiogram (ECG) was recently introduced as a biometric authentication system suitable for security checks. The proposed authentication system helps investigators studying ECG-based biometric authentication techniques to reshape input data by slicing based on the RR-interval, and defines the Overall Performance (OP), which is the combined performance metric of multiple authentication measures. We evaluated the performance of the proposed system using a confusion matrix and achieved up to 95% accuracy by compact data analysis. We also used the Amang ECG (amgecg) toolbox in MATLAB to investigate the upper-range control limit (UCL) based on the mean square error, which directly affects three authentication performance metrics: the accuracy, the number of accepted samples, and the OP. Using this approach, we found that the OP can be optimized by using a UCL of 0.0028, which indicates 61 accepted samples out of 70 and ensures that the proposed authentication system achieves an accuracy of 95%. INDEX TERMSauthentication, biomedical signal processing, electrocardiogram (ECG), R-R interval, identification, MATLAB, machine learning, multi-variable regression 2. VOLUME XX, 2017 9 FIGURE 6.Number of accepted samples based on the upper-range control limit..Alternatively, we can evaluate the relationship between the UCL and OP. We found that the optimal UCL based on accuracy was not the same as the optimal UCL based on the OP (Fig. 7). The optimal UCL associated with the best OP was 0.0028, which achieves 95% accuracy on the basis of 61 accepted samples from a total of 70. FIGURE 7. Authentication accuracy, the accepted sampling rate and OP based on UCL.The optimal UCL may not remain the same if different datasets are used, but our experiments clearly demonstrated that optimal values for these parameters exist and can be used to improve performance. V. CONCLUSIONBiometric authentication systems have many advantages over traditional systems, but the applications of ECG data depend on the particular use case and thus the nature of the authentication system. We have proposed a versatile RRIF biometric authentication system for the security check use case using a regression-based interpretable ML approach with the new OP measure based on the data quality (UCL). We trained on a total of 60 ECG data samples to generate the reference function database. The reference function for each ECG data entity was then generated using a mutual information-based DT regression 2. VOLUME XX, 2017 9

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“…Medical informatics with AI computation can be targeted in a recognition model to efficiently identify clinical data characteristics for health risk assessment and prevention [3][4][5]. The AI-enabled machine learning (ML) model typically consists of featuring and data training, which pre-processes the labeled samples (e.g., specific symptoms in the electrocardiogram (ECG) of arrhythmia) and explores the Sensors 2022, 22, 689 2 of 24 features to recognize clinical data [6][7][8]. Hybrid ML methods were qualified for coupling analysis of comprehensive features.…”

Section: Introductionmentioning

confidence: 99%

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

Hsiao

Kan

Kuo

et al. 2022

Sensors

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We established a web-based ubiquitous health management (UHM) system, “ECG4UHM”, for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert–Huang transform (HHT) with empirical mode decomposition to calculate ECGs’ intrinsic mode functions (IMFs). The area centroids of the IMFs’ marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLABTM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC–VPC) and the fibril-rapid pattern (i.e., AFib–VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC–VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.

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An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning

Cited by 72 publications

References 30 publications

Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

An Enhanced Machine Learning-Based Biometric Authentication System Using RR-Interval Framed Electrocardiograms

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

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