Wide machine learning algorithms evaluation applied to ECG authentication and gender recognition

ECG signals have been widely studied for knowing heart behavior and following cardiac abnormalities. Last years have emerged new applications where ECG has being used in cryptography and biometrics. The purpose in this paper center around perform two independent experiments taking advantage of the ECG properties. The first experiment is about person authentication and the second experiment covers gender recognition. Both tests are performed extracting the same features and evaluating the classification accuracy with several machine learning algorithms sensing the ECG signals in different body positions. ECG signal contains properties like liveness detection, ubiquity, difficulty of being copied, continuity, and reclaims the mandatory user presence. These properties makes ECG study having the potential of being embedded for smartphone applications in the Internet of Things era. The best accuracy score is over the 98% for ECG authentication and 94% for gender recognition; as the best of our knowledge there is no ECG gender recognition with the algorithms studied in this paper.