A method for identification/verification of an individual comprising: sampling HF-QRS signals or derived features or values from a group of subjects, which each sampling performed at several different times for each subject; using deep learning neural networks to determine features or values which are: (i) sufficiently similar for the same individual for samples taken at different times; and (ii) sufficiently different among all individuals whenever sampled, to function as biometric signatures; wherein the features or values which function as biometric signatures are determined by first establishing a training set of features or values from the group of subjects, with each subject's HF-QRS sampled a number of times; comparing the training set with a validation set for identifying overfitting of a classifier and for additional verification of suitable features or values, further including comparing the training set with a validation set for verification of the features or values which function as biometric signatures; further including reducing overfitting by regularization; sampling said features or values from individuals and storing them in a database as each of their respective biometric signatures; and sampling, at a later time, said features or values from an unknown individual and determining if they match any of the stored biometric signatures; finding a biometric signature using HF-QRS signals which is stable over time by monitoring the selected biometric signatures of a group of test subjects over time to find stable signature(s), and determining a deviation threshold from the monitoring such that said test subjects' stable signatures fall within the deviation threshold over the period measured; establishing a database of stable signatures derived from actual subjects; and monitoring the stable signatures of said actual subjects at least as often as said period. sampling HF-QRS signals or derived features or values from a group of subjects, which each sampling performed at several different times for each subject; using deep learning neural networks to determine features or values which are: (i) sufficiently similar for the same individual for samples taken at different times; and (ii) sufficiently different among all individuals whenever sampled, to function as biometric signatures; wherein the features or values which function as biometric signatures are determined by first establishing a training set of features or values from the group of subjects, with each subject's HF-QRS sampled a number of times; comparing the training set with a validation set for identifying overfitting of a classifier and for additional verification of suitable features or values, further including comparing the training set with a validation set for verification of the features or values which function as biometric signatures; (i) sufficiently similar for the same individual for samples taken at different times; and (ii) sufficiently different among all individuals whenever sampled, to function as biometric signatures; wherein the features or values which function as biometric signatures are determined by first establishing a training set of features or values from the group of subjects, with each subject's HF-QRS sampled a number of times; comparing the training set with a validation set for identifying overfitting of a classifier and for additional verification of suitable features or values, further including comparing the training set with a validation set for verification of the features or values which function as biometric signatures; further including reducing overfitting by regularization; sampling said features or values from individuals and storing them in a database as each of their respective biometric signatures; and sampling, at a later time, said features or values from an unknown individual and determining if they match any of the stored biometric signatures; finding a biometric signature using HF-QRS signals which is stable over time by monitoring the selected biometric signatures of a group of test subjects over time to find stable signature(s), and determining a deviation threshold from the monitoring such that said test subjects' stable signatures fall within the deviation threshold over the period measured; establishing a database of stable signatures derived from actual subjects; and monitoring the stable signatures of said actual subjects at least as often as said period.