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Entropy
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Entropy and Analysis of EEG/ECG
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Special Issue "Entropy and Analysis of EEG/ECG"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: 30 January 2024 | Viewed by 568

Special Issue Editors

Prof. Dr. Marcelo Risk
E-Mail Website
Guest Editor
Instituto de Medicina Traslacional e Ingenieria Biomedica (IMTIB), CONICET-IUHIBA-HIBA, Potosi 4240, Buenos Aires C1199, Argentina
Interests: biomedical signal processing; ECG analysis; heart rate variability; autonomic nervous system
Prof. Dr. Francisco O. Redelico
E-Mail Website
Guest Editor
Instituto de Medicina Traslacional e Ingenieria Biomedica (IMTIB), CONICET-IUHIBA-HIBA, Potosi 4240, Buenos Aires C1199, Argentina
Interests: biomedical signal processing; information theory; EEG analysis

Special Issue Information

Dear Colleagues,

Biomedical signals, such as electrocardiogram (ECG) and electroencephalogram (EEG), are windows to the electrical activities of the heart and brain, both in a noninvasive manner, which reflect the activities and influence of other organs such as the autonomic nervous system, among others, and can be used to study the healthy function as well the state of disease.

ECG and EEG can be analyzed using derived signals such as heart rate variability and energy of the corresponding frequency bands, respectively.

These signals exhibit nonlinear behaviors, which have been successfully analyzed using entropy-based quantifiers, fractals and other nonlinear techniques.

For this Special Issue, original contributions or reviews related to heart rate variability, morphological ECG analysis, dynamics of EEG sub-band energies, spike timing in EEG, etc., using nonlinear dynamic tools such as entropy, fractality, and others will be welcome.

Prof. Dr. Marcelo Risk
Prof. Dr. Francisco O. Redelico
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. Entropy is an international peer-reviewed open access monthly 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

  • heart rate variability, blood pressure variability, blood volume variability, and other cardiovascular time series
  • energy sub-band decomposition, inter-spike times, and others neurological time series
  • clinical applications of nonlinear times series analysis

Published Papers (1 paper)

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Article
Deep Learning Modeling of Cardiac Arrhythmia Classification on Information Feature Fusion Image with Attention Mechanism
by
Mingming Zhang
,
Huiyuan Jin
,
Bin Zheng
and
Wenbo Luo
Entropy 2023, 25(9), 1264; https://doi.org/10.3390/e25091264 - 26 Aug 2023
Viewed by 403
Abstract
The electrocardiogram (ECG) is a crucial tool for assessing cardiac health in humans. Aiming to enhance the accuracy of ECG signal classification, a novel approach is proposed based on relative position matrix and deep learning network information features for the classification task in [...] Read more.
The electrocardiogram (ECG) is a crucial tool for assessing cardiac health in humans. Aiming to enhance the accuracy of ECG signal classification, a novel approach is proposed based on relative position matrix and deep learning network information features for the classification task in this paper. The approach improves the feature extraction capability and classification accuracy via techniques of image conversion and attention mechanism. In terms of the recognition strategy, this paper presents an image conversion using relative position matrix information. This information is utilized to describe the relative spatial relationships between different waveforms, and the image identification is successfully applied to the Gam-Resnet18 deep learning network model with a transfer learning concept for classification. Ultimately, this model achieved a total accuracy of 99.30%, an average positive prediction rate of 98.76%, a sensitivity of 98.90%, and a specificity of 99.84% with the relative position matrix approach. To evaluate the effectiveness of the proposed method, different image conversion techniques are compared on the test set. The experimental results demonstrate that the relative position matrix information can better reflect the differences between various types of arrhythmias, thereby improving the accuracy and stability of classification. Full article
(This article belongs to the Special Issue Entropy and Analysis of EEG/ECG)
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