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Special Issue "Biomedical Signal Processing for Disease Diagnosis"

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

Deadline for manuscript submissions: 31 May 2020.

Special Issue Editors

Guest Editor
Prof. Dr. Carlos Gómez

Biomedical Engineering Group, Universidad de Valladolid, Paseo Belén, 15, 47011 Valladolid, Spain
Website | E-Mail
Interests: biomedical signals; signal processing; nonlinear analyses; connectivity measures; electroencephalography; magnetoencephalography
Guest Editor
Prof. Dr. Raúl Alcaraz

Research Group in Electronic, Biomedical and Telecommunication Engineering, Universidad de Castilla-La Mancha, Campus Universitario s/n, 16071, Cuenca
Website | E-Mail
Interests: entropy; complexity; information theory; information geometry; nonlinear dynamics; computational mathematics and statistics in medicine; biomedical time series analysis; cardiac signal processing

Special Issue Information

Dear Colleagues,

Nowadays, sensors are integrated in many medical devices in order to record the signals generated by the physiological activity of our body. Biomedical signal processing is an interdisciplinary field where physicians, mathematicians, biologists, and engineers, among others, collaborate to develop and/or apply mathematical methods to extract useful information from the recorded physiological data. This Special Issue aims to attract researchers with interest on the application of signal processing methods to different biomedical signals (electrocardiogram, electroencephalogram, magnetoencephalogram, electromyogram, galvanic skin response, pulse oximetry, photopletismogram, etc.) to help physicians in the diagnosis of human diseases. Original papers that describe new research on this subject are welcomed. We look forward your participation in this Special Issue.

Prof. Dr. Carlos Gómez
Prof. Dr. Raúl Alcaraz
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 papers will be 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 1800 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

  • Biomedical signal processing
  • Biomedical signals (ECG, EEG, PPG, EDR, EMG, etc.)
  • Diseases
  • Aid diagnosis
  • Physiological time series dynamics
  • Physiological redundancy, synergy, complexity, and connectivity
  • Linear and non-linear data processing
  • Time, frequency, and time–frequency analyses

Published Papers (1 paper)

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Research

Open AccessArticle
A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet
Sensors 2019, 19(14), 3214; https://doi.org/10.3390/s19143214
Received: 22 June 2019 / Revised: 13 July 2019 / Accepted: 19 July 2019 / Published: 21 July 2019
PDF Full-text (3987 KB) | HTML Full-text | XML Full-text
Abstract
Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning algorithm-based CVD diagnosis methods are [...] Read more.
Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning algorithm-based CVD diagnosis methods are extensively employed, however, most such algorithms can only utilize one-lead ECGs. Hence, the potential information in other-lead ECGs was not utilized. To address this issue, we have developed novel methods for diagnosing arrhythmia. In this work, DL-CCANet and TL-CCANet are proposed to extract abstract discriminating features from dual-lead and three-lead ECGs, respectively. Then, the linear support vector machine specializing in high-dimensional features is used as the classifier model. On the MIT-BIH database, a 95.2% overall accuracy is obtained by detecting 15 types of heartbeats using DL-CCANet. On the INCART database, overall accuracies of 94.01% (II and V1 leads), 93.90% (V1 and V5 leads) and 94.07% (II and V5 leads) are achieved by detecting seven types of heartbeat using DL-CCANet, while TL-CCANet yields a higher overall accuracy of 95.52% using the above three leads. In addition, all of the above experiments are implemented using noisy ECG data. The proposed methods have potential to be applied in the clinic and mobile devices. Full article
(This article belongs to the Special Issue Biomedical Signal Processing for Disease Diagnosis)
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