Published Paper Details:

This project introduces a framework for appropriately adapting and adjusting machine learning (ML) techniques used to construct electrocardiogram (ECG)-based biometric authentication schemes. It can help define the boundaries of required datasets and get training data with good quality. Use case analysis is adopted to determine the boundaries of datasets. Based on various application scenarios on ECG-based authentication, three different use cases 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. 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.