11. A training method comprising: receiving electrocardiogram (ECG) training data; augmenting the ECG data; and filtering the ECG signal using a band pass filter; training a neural network model for ECG authentication based on the augmented ECG training data; training candidate neural network models for the augmented ECG training data; selecting a candidate neural network model from the candidate neural network models by minimizing a cross-entropy based on an uppermost layer of the candidate neural network model, wherein the augmenting comprises detecting a fiducial point from the ECG data after filtering and acquiring a data segment from the filtered ECG signal based on the fiducial point.
11. A training method comprising: receiving electrocardiogram (ECG) training data; augmenting the ECG data; and filtering the ECG signal using a band pass filter; training a neural network model for ECG authentication based on the augmented ECG training data; training candidate neural network models for the augmented ECG training data; selecting a candidate neural network model from the candidate neural network models by minimizing a cross-entropy based on an uppermost layer of the candidate neural network model, wherein the augmenting comprises detecting a fiducial point from the ECG data after filtering and acquiring a data segment from the filtered ECG signal based on the fiducial point. 15. The training method of claim 11 , wherein the augmenting of the ECG training data further comprises: normalizing a data segment obtained through the offset.