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Special Issue "ECG Sensors"

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

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editors

Prof. Laura Burattini
E-Mail Website
Guest Editor
Università Politecnica delle Marche, Ancona, Italy
Interests: cardiovascualar signal processing; clinical ECG interpretation; fetal ECG; newborn ECG; sport applications
Prof. Jean Philippe Couderc
E-Mail Website
Guest Editor
University of Rochester, NY, USA
Interests: ECG signal processing; wearable sensors; computerized technologies for cardiac safety
Prof. Danilo Pani
E-Mail Website
Guest Editor
University of Cagliari, Italy
Interests: wearable electronics; textile sensors; biomedical signal processing; fetal ECG; physiology of taste; neural signal decoding; telehealth; telerehabilitation
Special Issues and Collections in MDPI journals
Prof. Paola Pierleoni
E-Mail Website
Guest Editor
Università Politecnica delle Marche, Italy
Interests: wearable sensors; wireless body sensor networks; Internet of Things; health monitoring
Special Issues and Collections in MDPI journals
Dr. Micaela Morettini
E-Mail Website
Guest Editor
Università Politecnica delle Marche, Italy
Interests: biological systems modeling; sport applications; biomedical signal processing; health monitoring

Special Issue Information

Dear Colleagues,

The electrocardiogram (ECG) represents a simple, cheap, and non-invasive diagnostic examination for assessing the functionality of the electrical system of the heart. It allows discovering pathological conditions even before structural changes in the heart can be diagnosed by other methods. The reduced data size of the recordings, due to the typical sampling frequency and number of channels, makes its adoption possible in any scenario, including telehealth and telecare, where unobtrusiveness of the measuring system is crucial. These characteristics, along with the possibilities offered by the miniaturization of the recording systems, fostered the development of diagnostic and monitoring devices for long-term ECG recording, which opened the possibility of wearing ECG devices for personal or clinical use. Then, the technological challenge moved from the realization of lightweight ECG devices to the realization of unobtrusive electrodes. Each wave of the ECG tracing reflects a specific phase of the cardiac cycle; thus, analysis of the ECG morphological and temporal features provides important clinical information on the health status of the heart at any age and condition. ECG interpretation may be jeopardized by artefact and noise affecting the ECG tracing. Consequently, ECG sensors have to be designed in order to minimize noise during acquisition, specific hardware and software filters have to be implemented for ECG cleaning, and signal processing procedures have to be implemented for clinical information extraction. Moreover, some specialistic clinical uses (such as multimodal recordings) find severe limitation in the current electrode technologies, asking for further research on this fundamental aspect, designing electrodes with specific physical characteristics, or including some stages of the signal acquisition chain close to the acquisition point on the body.

This Special Issue aims to collect original research papers or review papers on advances in the technologies for the design of ECG electrodes. Moreover, readout electronic circuitry for ECG electrodes, and novel techniques for processing of the ECG signals gathered with such tools are also welcome. Topics include but are not limited to:

  • ECG electrodes materials and technologies;
  • ECG electrodes characterization;
  • Textile electrodes;
  • ECG electrodes on unconventional substrates;
  • Loop recorders and invasive devices;
  • Cardiac catheters for intracardiac recording and stimulation;
  • Embedded systems for ECG sensing;
  • Hardware front-end for ECG electrodes;
  • Active ECG electrodes;
  • Hardware and software filtering for ECG denoising and enhancement;
  • Fetal electrocardiography electrodes and systems;
  • Newborn and pediatric electrocardiography electrodes and systems;
  • ECG applications during physical activity;
  • Long-term ECG recording systems;
  • Cardiac home telemonitoring systems;
  • Sensor-dependent signal processing for computer-aided diagnosis.

Prof. Laura Burattini
Prof. Jean Philippe Couderc
Prof. Danilo Pani
Prof. Paola Pierleoni
Dr. Micaela Morettini
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 2000 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

  • ECG electrodes
  • electrodes technology
  • electrodes materials
  • ECG acquisition
  • ECG front-end
  • active electrodes
  • ECG processing

Published Papers (2 papers)

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Research

Open AccessArticle
Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals
Sensors 2020, 20(4), 1020; https://doi.org/10.3390/s20041020 - 14 Feb 2020
Abstract
The electrocardiogram (ECG) is a non-invasive, inexpensive, and effective tool for myocardial infarction (MI) diagnosis. Conventional detection algorithms require solid domain expertise and rely heavily on handcrafted features. Although previous works have studied deep learning methods for extracting features, these methods still neglect [...] Read more.
The electrocardiogram (ECG) is a non-invasive, inexpensive, and effective tool for myocardial infarction (MI) diagnosis. Conventional detection algorithms require solid domain expertise and rely heavily on handcrafted features. Although previous works have studied deep learning methods for extracting features, these methods still neglect the relationships between different leads and the temporal characteristics of ECG signals. To handle the issues, a novel multi-lead attention (MLA) mechanism integrated with convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) framework (MLA-CNN-BiGRU) is therefore proposed to detect and locate MI via 12-lead ECG records. Specifically, the MLA mechanism automatically measures and assigns the weights to different leads according to their contribution. The two-dimensional CNN module exploits the interrelated characteristics between leads and extracts discriminative spatial features. Moreover, the BiGRU module extracts essential temporal features inside each lead. The spatial and temporal features from these two modules are fused together as global features for classification. In experiments, MI location and detection were performed under both intra-patient scheme and inter-patient scheme to test the robustness of the proposed framework. Experimental results indicate that our intelligent framework achieved satisfactory performance and demonstrated vital clinical significance. Full article
(This article belongs to the Special Issue ECG Sensors)
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Open AccessArticle
A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection
Sensors 2020, 20(1), 9; https://doi.org/10.3390/s20010009 - 18 Dec 2019
Abstract
Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event [...] Read more.
Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works. Full article
(This article belongs to the Special Issue ECG Sensors)
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