Feature Selection as a Preprocessing Step for Hierarchical Clustering
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Verification of humans using the electrocardiogram
Pattern Recognition Letters
Dependency-based feature selection for clustering symbolic data
Intelligent Data Analysis
Remote health-care monitoring using personal care connect
IBM Systems Journal
EURASIP Journal on Advances in Signal Processing
A novel wavelet packet-based anti-spoofing technique to secure ECG data
International Journal of Biometrics
A New Feature Detection Mechanism and Its Application in Secured ECG Transmission with Noise Masking
Journal of Medical Systems
Novel methods of faster cardiovascular diagnosis in wireless telecardiology
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
Pattern Recognition
A Mobile Care System With Alert Mechanism
IEEE Transactions on Information Technology in Biomedicine
Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment
IEEE Transactions on Fuzzy Systems
A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health
IEEE Communications Magazine
Biometric verification of subjects using saccade eye movements
International Journal of Biometrics
Biometric verification of a subject through eye movements
Computers in Biology and Medicine
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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.