10th Anniversary of Bioengineering: Biosignal Processing

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 381

Special Issue Editors


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Guest Editor
Department of Innovation Engineering (DII), University of Salento, Via Monteroni, 73100 Lecce, Italy
Interests: fault detection; sensor technologies; measurement techniques; monitoring and measurement systems; testing and characterization components; systems and monitoring equipment
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
Interests: mathematical modeling; signal and image processing; radiomics; systems and synthetic biology; physiological control systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Innovation Engineering (DII), University of Salento, Via Monteroni, 73100 Lecce, Italy
Interests: biosignal processing; sensor technologies; measurement techniques; monitoring and measurement systems; medical diagnostics; systems and monitoring equipment

Special Issue Information

Dear Colleagues,

The field of bioengineering has come a long way since its inception, and one of the most significant advancements in recent years has been in the area of biosignal processing. Biosignal processing involves the acquisition, analysis, and interpretation of signals generated by the human body, such as electrocardiograms (ECGs), electroencephalograms (EEGs), and electromyograms (EMGs), among others.

Over the past decade, bioengineers have made remarkable progress in developing sophisticated algorithms and techniques to extract useful information from biosignals. These signals, once processed, can provide valuable insights into various physiological and pathological conditions, enabling early diagnosis, monitoring of diseases, and personalized treatment approaches. One major breakthrough in biosignal processing has been the development of advanced signal processing algorithms. These algorithms, often based on machine learning and artificial intelligence techniques, enable the extraction of valuable features from biosignals that are not easily discernible through conventional methods. For example, these algorithms can be used to identify specific patterns in an ECG that may indicate the presence of cardiac abnormalities. Additionally, bioengineers have been successful in developing non-invasive biosignal processing techniques. These techniques allow for the acquisition of biosignals without requiring invasive procedures or expensive medical equipment. For instance, wearable biosensors and mobile health applications have made it possible to continuously monitor biosignals, such as heart rate and sleep patterns, in real time and in everyday environments. Furthermore, the integration of biosignal processing with other fields, such as bioinformatics and genomics, has opened up new avenues for research and innovation. By combining biosignal data with genomic information, bioengineers can gain a deeper understanding of the relationship between genetic factors and physiological responses, leading to the development of personalized medicine strategies.

Looking ahead, the future of biosignal processing in bioengineering appears promising. Researchers are continuing to refine and improve existing algorithms, making them more accurate and robust.

Additionally, biosensors and wearable devices, along with measurement, instrumentation, sensing, and diagnostics of biosignals, are all aspects that play a crucial role in the analysis and interpretation of various biosignals that provide valuable information about a person's health status and can help in diagnosis, monitoring, healthcare assessments, early detection of diseases, and the development of personalized treatment strategies.

In conclusion, the 10th anniversary of bioengineering biosignal processing marks a significant milestone in the field's advancement. The progress made over the past decade has revolutionized healthcare by enabling the extraction of valuable information from biosignals. With further advancements on the horizon, biosignal processing will continue to play a crucial role in improving the diagnosis, treatment, and monitoring of diseases.

Dr. Andrea Cataldo
Prof. Dr. Francesco Amato
Dr. Raissa Schiavoni
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. Bioengineering 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 2700 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

  • biosignal processing
  • signal analysis
  • biomedical devices
  • medical diagnostics
  • biomedical imaging
  • biomedical engineering
  • health monitoring
  • wearable systems
  • image processing and visualization
  • biosensor technology
  • biosensors
  • lab-on-chip and organ-on-a-chip instrumentation
  • smart sensing and predictive modeling
  • 4.0-ehanced biomedical systems

Published Papers (1 paper)

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17 pages, 7424 KiB  
Article
Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data
by Ruochen Zhao, Ruonan Wang, Yang Gao and Xiaolin Ning
Bioengineering 2024, 11(5), 428; https://doi.org/10.3390/bioengineering11050428 - 26 Apr 2024
Viewed by 182
Abstract
A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal [...] Read more.
A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal distortion. However, most current methods for estimating the dimension threshold rely on experience, such as observing the signal waveforms and spectrum, which may render the results too subjective and lacking in quantitative accuracy. Therefore, this study proposes a method to automatically estimate a suitable threshold. Time–frequency transformations are performed on the evoked state data to obtain the neural signal of interest and the noise signal in a specific time–frequency band, which are then used to construct the objective function describing the degree of noise suppression and signal distortion. The optimal value of the threshold in the selected range is obtained using the weighted-sum method. Our method was tested on two classical subspace projection algorithms using simulation and two sensory stimulation experiments. The thresholds estimated by the proposed method enabled the algorithms to achieve the best waveform recovery and source location error. Therefore, the threshold selected in this method enables subspace projection algorithms to achieve the best balance between noise removal and neural signal preservation in subsequent MEG analyses. Full article
(This article belongs to the Special Issue 10th Anniversary of Bioengineering: Biosignal Processing)
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