This paper introduces a framework for how to appropriately adopt and adjust machine learning (ML) techniques used to construct electrocardiogram (ECG)-based biometric authentication schemes. The proposed framework can help investigators and developers on ECG-based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality. To determine the boundaries of datasets, use case analysis is adopted. Based on various application scenarios on ECG-based authentication, three distinct use cases (or authentication categories) are developed. With more qualified training data given to corresponding machine learning schemes, the precision on ML-based ECG biometric authentication mechanisms are increased in consequence. The ECG time slicing technique with the R-peak anchoring is utilized in this framework to acquire ML training data with good quality. In the proposed framework four new measure metrics are introduced to evaluate the quality of the ML training and testing data. In addition, a Matlab toolbox, containing all proposed mechanisms, metrics, and sample data with demonstrations using various ML techniques, is developed and made publicly available for further investigation. For developing ML-based ECG biometric authentication, the proposed framework can guide researchers to prepare the proper ML setups and the ML training datasets along with three identified user case scenarios. For researchers adopting ML techniques to design new schemes in other research domains, the proposed framework is still useful for generating the ML-based training and testing datasets with good quality and utilizing new measure metrics.

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Traditional authentication systems use alphanumeric or graphical passwords, or token-based techniques that require ''something you know and something you have''. The disadvantages of these systems include the risks of forgetfulness, loss, and theft. To address these shortcomings, biometric authentication is rapidly replacing traditional authentication methods and is becoming a part of everyday life. The electrocardiogram (ECG) is one of the most recent traits considered for biometric purposes. In this work we describe an ECG-based authentication system suitable for security checks and hospital environments. The proposed system will help investigators studying ECG-based biometric authentication techniques to define dataset boundaries and to acquire high-quality training data. We evaluated the performance of the proposed system and found that it could achieve up to the 92% identification accuracy. In addition, by applying the Amang ECG (amgecg) toolbox within MATLAB, we investigated the two parameters that directly affect the accuracy of authentication: the ECG slicing time (sliding window) and the sampling time period, and found their optimal values. INDEX TERMS Authentication, biomedical signal processing, electrocardiogram signal (ECG), machine learning, multi-variable regression.

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