Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.


Pulse Active Transform (PAT): A non-invertible transformation with application to ECG biometric authentication

Bin Safie, Sairul Izwan and Nurfazira, H and Azavitra, Z and Soraghan, John and Petropoulakis, Lykourgos (2014) Pulse Active Transform (PAT): A non-invertible transformation with application to ECG biometric authentication. In: Region 10 Symposium, Kuala Lumpur, Malaysia. IEEE, 667 - 671. ISBN 978-1-4799-2028-0

PDF (Sairul-etal-IEEE2014-PAT-non-invertible-transformatio-biometric-authenticationn)
Sairul_etal_IEEE2014_PAT_non_invertible_transformatio_biometric_authenticationn.pdf - Accepted Author Manuscript

Download (219kB) | Preview


This paper presents a new transformation technique called the Pulse Active transform (PAT). The PAT uses a series of harmonically related periodic triangular waveforms to decompose a signal into a finite set of pulse active features. These features incorporate the signal's information in the pulse active domain, and which are subsequently processed for some desired application. PAT is non-invertible thus ensuring complete security of the original signal source. In this paper PAT is demonstrated on an ECG signal and used for biometric authentication. The new transformation technique is tested on 112 PTB subjects. It is shown in this paper that the new transformation has a superior performance compared to the conventional characteristic based feature extraction methods with additional security to avoid recovery of the original ECG.