Compression-Based Classification of ECG Using First-Order Derivatives
Due to its characteristics, there is a trend in biometrics to use the ECG signal for personal identification. There are different applications for this, namely, adapting entertainment systems to personal settings automatically.
Recent works based on compression models have shown that these approaches are suitable to ECG biometric identification. However, the best results are usually achieved by the methods that, at least, rely on one point of interest of the ECG – called fiducial methods.
In this work, we propose a compression-based non-fiducial method, that uses a measure of similarity, called the Normalized Relative Compression—a measure related to the Kolmogorov complexity of strings. Our method uses extended-alphabet finite-context models (xaFCMs) on the quantized first-order derivative of the signal, instead of using directly the original signal, as other methods do.
We were able to achieve state-of-the-art results on a database collected at the University of Aveiro, which was used on previous works, making it a good preliminary benchmark for the method.
KeywordsKolmogorov complexity Signal processing Compression Compression metrics Classification ECG Biometrics
This work was partially supported by national funds through the FCT–Foundation for Science and Technology, and by European funds through FEDER, under the COMPETE 2020 and Portugal 2020 programs, in the context of the projects UID/CEC/00127/2013 and PTDC/EEI-SII/6608/2014. S. Brás acknowledges the Postdoc Grant from FCT, ref. SFRH/BPD/92342/2013. The authors also wish to thank Dr. Sandra C. Soares and Jacqueline Ferreira, from the Education and Psychology Department of the University of Aveiro, for all the work building the ECG database which we used on this work.
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