Literature DB >> 24377903

Check your biosignals here: a new dataset for off-the-person ECG biometrics.

Hugo Plácido da Silva1, André Lourenço2, Ana Fred3, Nuno Raposo4, Marta Aires-de-Sousa5.   

Abstract

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.
Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Biometrics; Dataset; Electrocardiography; Off-the-person

Mesh:

Year:  2013        PMID: 24377903     DOI: 10.1016/j.cmpb.2013.11.017

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  6 in total

1.  ECG Biometrics Using Deep Learning and Relative Score Threshold Classification.

Authors:  David Belo; Nuno Bento; Hugo Silva; Ana Fred; Hugo Gamboa
Journal:  Sensors (Basel)       Date:  2020-07-22       Impact factor: 3.576

Review 2.  Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence- A Systematic Review.

Authors:  Paweł Jemioło; Dawid Storman; Maria Mamica; Mateusz Szymkowski; Wioletta Żabicka; Magdalena Wojtaszek-Główka; Antoni Ligęza
Journal:  Sensors (Basel)       Date:  2022-03-25       Impact factor: 3.576

3.  QRS detection and classification in Holter ECG data in one inference step.

Authors:  Adam Ivora; Ivo Viscor; Petr Nejedly; Radovan Smisek; Zuzana Koscova; Veronika Bulkova; Josef Halamek; Pavel Jurak; Filip Plesinger
Journal:  Sci Rep       Date:  2022-07-25       Impact factor: 4.996

Review 4.  State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.

Authors:  Georgios Petmezas; Leandros Stefanopoulos; Vassilis Kilintzis; Andreas Tzavelis; John A Rogers; Aggelos K Katsaggelos; Nicos Maglaveras
Journal:  JMIR Med Inform       Date:  2022-08-15

5.  Towards better heartbeat segmentation with deep learning classification.

Authors:  Pedro Silva; Eduardo Luz; Guilherme Silva; Gladston Moreira; Elizabeth Wanner; Flavio Vidal; David Menotti
Journal:  Sci Rep       Date:  2020-11-26       Impact factor: 4.379

6.  Initial Study Using Electrocardiogram for Authentication and Identification.

Authors:  Teresa M C Pereira; Raquel C Conceição; Raquel Sebastião
Journal:  Sensors (Basel)       Date:  2022-03-11       Impact factor: 3.576

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.