  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 is increased in
consequence. 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 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 ML-based
training and testing datasets with good quality and utilizing new measure
metrics.
