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Electronics
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New Challenges in Biomedical Signal Processing: Computational Theory and Applications
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New Challenges in Biomedical Signal Processing: Computational Theory and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 5599

Special Issue Editors

Dr. Alessandro Mengarelli

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy
Interests: biomechanics; movement analysis; advanced signal processing; biomechanical modeling and control
Special Issues, Collections and Topics in MDPI journals
Dr. Federica Verdini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche, 60131 Ancona, Italy
Interests: biomechanics; movement analysis; advanced signal processing; biomechanical modeling and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Throughout the years, signal processing has maintained a central role in the biomedical research and application field, being one of the fundamental aspects for extracting information from raw biological data. Nowadays, the dramatic increase in biological sensing technology has expanded the landscape of possible biomedical applications, ranging from measuring and monitoring to the assessment of human physical activities and physiological conditions. Therefore, different sensors for signal recording warrant different and specific signal treatment procedures, and reliable signal processing techniques are required for a number of different applications, such as movement analysis, biosignals interpretation (EEG, ECG, EMG), image processing, and inertial sensing. Further, the central role gained in the last few years by artificial intelligence techniques for biomedical purposes has even further enhanced the need for processing techniques suited to specific contexts, in order to derive significant signal features.

The aim of this Special Issue is to present high-quality papers or critical reviews dealing with cutting-edge developments in biomedical signal processing, in addition to proposing significant applications in the field of clinically oriented devices for rehabilitation and diagnostic purposes.

The topics of interest include, but are not limited to:

  • Non-linear signal processing
  • Filtering techniques
  • Optimization techniques
  • Bio-signals processing
  • Big data analysis in biomedical applications
  • Image processing
  • Timeseries analysis
  • Patterns recognition in biological data
Dr. Alessandro Mengarelli
Dr. Federica Verdini
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. Electronics 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 2400 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.

Published Papers (2 papers)

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Research

14 pages, 3949 KiB  
Article
Identification of Prognostic Factors and Predicting the Therapeutic Effect of Laser Photocoagulation for DME Treatment
by Nataly Ilyasova, Aleksandr Shirokanev, Dmitriy Kirsh, Nikita Demin, Evgeniy Zamytskiy, Rustam Paringer and Alexey Antonov
Electronics 2021, 10(12), 1420; https://doi.org/10.3390/electronics10121420 - 12 Jun 2021
Cited by 5 | Viewed by 1632
Abstract
Diabetic retinopathy is among the most severe complications of diabetes, most often leading to rapid and irreversible vision loss. The laser coagulation procedure, which consists of applying microburns to the fundus, has proven to be an effective method for treating diabetic retinopathy. Unfortunately, [...] Read more.
Diabetic retinopathy is among the most severe complications of diabetes, most often leading to rapid and irreversible vision loss. The laser coagulation procedure, which consists of applying microburns to the fundus, has proven to be an effective method for treating diabetic retinopathy. Unfortunately, modern research does not pay enough attention to the study of the arrangement of microburns in the edema area—One of the key factors affecting the quality of therapy. The aim of this study was to propose a computational decision-making support system for retina laser photocoagulation based on the analysis of photocoagulation plans. Firstly, we investigated a set of prognostic factors based on 29 features describing the geometric arrangement of coagulates. Secondly, we designed a technology for the intelligent analysis of the photocoagulation plan that allows the effectiveness of the treatment to be predicted. The studies were carried out using a large database of fundus images from 108 patients collected in clinical trials. The results demonstrated a high classification accuracy at a level of over 85% using the proposed prognostic factors. Moreover, the designed technology proved the superiority of the proposed algorithms for the automatic arrangement of coagulates, predicting a 99% chance of a positive therapeutic effect. Full article
(This article belongs to the Special Issue New Challenges in Biomedical Signal Processing: Computational Theory and Applications)
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17 pages, 1664 KiB  
Article
Wavelet Transform Analysis of Heart Rate to Assess Recovery Time for Long Distance Runners
by Grzegorz Redlarski, Janusz Siebert, Marek Krawczuk, Arkadiusz Zak, Ludmila Danilowicz-Szymanowicz, Lukasz Dolinski, Piotr Gutknecht, Bartosz Trzeciak, Wojciech Ratkowski and Aleksander Palkowski
Electronics 2020, 9(12), 2189; https://doi.org/10.3390/electronics9122189 - 18 Dec 2020
Cited by 1 | Viewed by 2543
Abstract
The diagnostics of the condition of athletes has become a field of special scientific interest and activity. The aim of this study was to verify the effect of a long (100 km) run on a group of runners, as well as to assess [...] Read more.
The diagnostics of the condition of athletes has become a field of special scientific interest and activity. The aim of this study was to verify the effect of a long (100 km) run on a group of runners, as well as to assess the recovery time that is required for them to return to the pre-run state. The heart rate (HR) data presented were collected the day before the extreme physical effort, on the same day as, but after, the physical effort, as well as 24 and 48 h after. The Wavelet Transform (WT) and the Wavelet-based Fractal Analysis (WBFA) were implemented in the analysis. A tool was constructed that, based on quantitative data, enables one to confirm the completion of the recovery process that is related to the extreme physical effort. Indirectly, a tool was constructed that enables one to confirm the completion of the recovery process. The obtained information proves that the return to the resting state of the body after a significant physical effort can be observed after two days entirely through the analysis of the HR. Certain practical measures were used to differentiate between two substantially different states of the human body, i.e., pre- and post-effort states were constructed. The obtained results allow for us to state that WBFA appears to be a useful and robust tool in the determination of hidden features of stochastic signals, such as HR time signals. The proposed method allows one to differentiate between particular days of measurements with a mean probability of 92.2%. Full article
(This article belongs to the Special Issue New Challenges in Biomedical Signal Processing: Computational Theory and Applications)
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