Pattern Recognition Letters - In memory of Professor E.S. Gelsema
One Lead ECG Based Personal Identification with Feature Subspace Ensembles
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Pattern Recognition
Robust ECG Biometrics by Fusing Temporal and Cepstral Information
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
In-vehicle driver recognition based on hand ECG signals
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Clinical data privacy and customization via biometrics based on ECG signals
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
Check Your Biosignals Here: A new dataset for off-the-person ECG biometrics
Computer Methods and Programs in Biomedicine
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The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.