Analysis of human electrocardiogram for biometric recognition
EURASIP Journal on Advances in Signal Processing
UAHCI '09 Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Addressing Diversity. Part I: Held as Part of HCI International 2009
Unveiling the biometric potential of finger-based ECG signals
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
Introduction to Biometrics
In-vehicle driver recognition based on hand ECG signals
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
ECG Pattern Analysis for Emotion Detection
IEEE Transactions on Affective Computing
Efficient data management in a large-scale epidemiology research project
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Online and offline determination of QT and PR interval and QRS duration in electrocardiography
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Computer Methods and Programs in Biomedicine
Multistage approach for clustering and classification of ECG data
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
The Check Your Biosignals Here initiative (CYBHi) was developed as a way of creating a dataset and consistently repeatable acquisition framework, to further extend research in electrocardiographic (ECG) biometrics. In particular, our work targets the novel trend towards off-the-person data acquisition, which opens a broad new set of challenges and opportunities both for research and industry. While datasets with ECG signals collected using medical grade equipment at the chest can be easily found, for off-the-person ECG data the solution is generally for each team to collect their own corpus at considerable expense of resources. In this paper we describe the context, experimental considerations, methods, and preliminary findings of two public datasets created by our team, one for short-term and another for long-term assessment, with ECG data collected at the hand palms and fingers.