sensors-logo

Journal Browser

Journal Browser

Bio-Electronics in Healthcare: Biosensors and Biomedical Signal Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (1 June 2021) | Viewed by 4465

Special Issue Editors


E-Mail Website
Guest Editor

E-Mail Website
Guest Editor
Sensing, Electronics, and Embedded Systems, University of Cyprus, Nicosia 1678, Cyprus
Interests: wearable sensors; printed sensors; electrochemical sensors and electronic sensor interfaces

Special Issue Information

Dear Colleagues,

Electronics and sensors in healthcare are driving forces for improving quality of care and safety. From wearable devices to the operating theatre and implants, electronics and sensors are playing a vital role. Miniaturisation has led to more implantable devices for accurate in-vivo measurement. In some of these applications, it is advantageous to have flexible devices. This can include solutions such as completely organic electronics or thinned/small silicon chips mounted on flexible substrates. Some of these devices, for implants, can be made to be bio-degradable so that the body absorbs the devices at the end of their useful lives. It is also essential to have good signal processing. In the case of implants, this should be low-power and efficient, due to the low levels of energy available. This Special Issue is designed to attract papers covering the whole range of the system, from sensors to read-out and further signal processing and communication. Aspects such as special packaging techniques and power systems are also welcome.

Prof. Dr. Paddy J. French
Dr. Marios Sophocleous
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • in-vivo sensors
  • bioelectronics
  • medical sensors
  • communication for implants
  • medical sensor systems

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 8831 KiB  
Article
Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning
by Junmo Kim, Geunbo Yang, Juhyeong Kim, Seungmin Lee, Ko Keun Kim and Cheolsoo Park
Sensors 2021, 21(5), 1568; https://doi.org/10.3390/s21051568 (registering DOI) - 24 Feb 2021
Cited by 13 | Viewed by 3577
Abstract
Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs [...] Read more.
Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days. Full article
Show Figures

Figure 1

Back to TopTop