An ECG Deep Learning user identification architecture using ECG sex recognition as a selective parameter

Background:

Human user authentication can be implemented by token-, keyword-, or identity-based mechanisms for digital environment session entry (i.e., smartphones, platforms with log-in). Physiological signals, such as ECG, have shown discriminative properties for user identity recognition. Due to ECG hidden nature, it is resilient to public trait exposition, light/noise saturation, or eavesdropping in contrast to fingerprint, facial, voice, or password approaches. ECG might fill those gaps toward a cooperative authentication environment.

Methods:

This paper proposes a Deep Learning identification scenario in which the inclusion of sex recognition directs the input sample toward a sex-specialized identity classification model, simplifying the discrimination space for each model. The architecture proposed could be suitable for large populations. Our scheme worked with an ECG three-axis pseudo-orthogonal configuration in which each axis is transformed into a time-frequency space. Additionally, we combine each lead matrix in an RGB image, joining the contribution of each wavelet waveform.

Results:

Our results suggest that it is possible to identify people by using RGB wavelet representations, achieving a classification average of 99.97%. In addition, the inclusion of the sex category for our identification purpose does not significantly affect the classification performance, making it a feasible solution for systems with a larger population.

Conclusions:

With the features of our database, we have evidence that it is possible to recognize a person’s identity using an ECG sex recognition module through our proposed architecture.