Abstract: Novel biometric systems have emerged in recent years as an alternative or complement to traditional identification systems based on passwords (something you know) or tokens (something you have). In this sense, biopotentials signals such as electrocardiograms (cardiac signal) or electroencephalograms (brain signals) have attracted many researchers' attention. This work proposes an innovative identification technique based on electrocardiograms (ECGs) and musical features (e.g., dynamics, rhythm or timbre) commonly used to characterise audio files. In a nutshell, after pre-processing ECG recordings, we transform them into audio wave files, split them into segments, extract features into five musical dimensions and finally fed a classifier with these instances. The proposal's workability is confirmed by experimentation using the MIT-BIH Normal Sinus Rhythm Database with 18 subjets and offering an accuracy of 96.6 and a low error rate with FAR and FRR 0.002 and 0.004, respectively.

Keywords: ECG; Audio; Biometrics; Identification; Artificial Intelligence; Pattern Recognition

Acknowledgements: (Optional)
This work was supported by Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation (P2019-CARDIOSEC); by the Spanish Ministry of Science, Innovation and Universities grant PID2019-111429RBC21(ODIO); and by the Comunidad de Madrid (Spain) under the project CYNAMON (P2018/TCS-4566), co-financed by European Structural Funds (ESF and FEDER).

Biography: Pedro Peris-Lopez received an M.Sc. degree in telecommunications engineering and a PhD in computer science from the Carlos III University of Madrid, Spain, in 2004 and 2008, respectively. He is currently an Associate Professor (accredited as a full professor) with the Department of Computer Science, Carlos III University of Madrid. His research interests are in the field of cybersecurity and e-health, digital forensics, and hardware security. He has published many articles in specialized journals (66) and conference proceedings (45). His works have more than 5500 citations, and his h-index is 34.

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