Faster person identification using compressed ECG in time critical wireless telecardiology applications

Adoption of compression technology is often required for wireless cardiovascular monitoring, due to the enormous size of electrocardiogram (ECG) signal and limited bandwidth of Internet. However, compressed ECG must be decompressed before performing human identification using present research on ECG based biometric techniques. This additional step of decompression creates a significant processing delay for identification task. This becomes an obvious burden on a system if this needs to be done for millions of compressed ECG segments by the hospital. This paper proposes a novel method of ECG biometric directly form compressed ECG harnessing data mining (DM) techniques like attribute selection and clustering. The biometric template created by this new technique is lower in size compared to the existing ECG based biometrics as well as other forms of biometrics like face, finger, retina, etc. The template size (and also the matching time) is up to 8533 times lower than face template, 61 times lower than existing percentage root mean square (PRD) ECG based biometric template and 9 times smaller than polynomial distance measurement (PDM) based ECG biometric. Smaller template size substantially reduces the one to many matching time for biometric recognition, resulting in a faster biometric authentication mechanism and ECG stream verification directly from compressed ECG. Crown Copyright & 2010 Published by Elsevier Ltd. All rights reserved.