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Applied Sciences
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Advances in Biomedical Signal Processing
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Advances in Biomedical Signal Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 October 2020)

Special Issue Editor

Prof. Dr. Fabio La Foresta

E-Mail Website
Guest Editor
Department of Civil, Energy, Environmental and Materials Engineering (DICEAM), Mediterranea University of Reggio Calabria, 89060 Reggio Calabria, Italy
Interests: electrical engineering; biomedical signal and image processing; artificial intelligence; neural networks; multidimensional and multiresolution analysis; non linear time series prediction and modeling; nonlinear dynamics; computational neural engineering; non-destructive testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, biomedical signal processing has brought relevant progress for solving several problems in many areas of biomedical engineering. Today more than ever, the extraction of information hidden in bio-signals plays a crucial role in understanding the secrets of how our body works. Despite the recent impressive progress, new diseases represent a future challenge and biomedical signal processing will continue to play an irreplaceable role for early diagnosis.

The aim of this Special Issue is to present and discuss the most recent advances in biomedical signal analysis and processing. I am inviting original research work including novel theories, innovative methods, and advanced systems that introduce significant advances in applied biosciences and bioengineering.

Prof. Ing. Fabio La Foresta
Guest Editor

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. Applied Sciences 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.

Keywords

  • multidimensional bio-signals processing
  • non-stationary bio-signals analysis
  • advanced systems for bio-signal prediction
  • reconstruction of bio-electric sources
  • bio-signals modeling
  • automatic systems for artefacts reduction
  • wearable medical devices

Published Papers (2 papers)

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19 pages, 736 KiB  
Article
An eLORETA Longitudinal Analysis of Resting State EEG Rhythms in Alzheimer’s Disease
by Serena Dattola and Fabio La Foresta
Appl. Sci. 2020, 10(16), 5666; https://doi.org/10.3390/app10165666 - 15 Aug 2020
Cited by 3 | Viewed by 2880
Abstract
Alzheimer’s disease (AD) is a degenerative brain disorder which is the most common cause of dementia. As there is no cure for AD, an early diagnosis is essential to slow down the progression of the disease with a proper pharmacological treatment. Electroencephalography (EEG) [...] Read more.
Alzheimer’s disease (AD) is a degenerative brain disorder which is the most common cause of dementia. As there is no cure for AD, an early diagnosis is essential to slow down the progression of the disease with a proper pharmacological treatment. Electroencephalography (EEG) represents a valid tool for studying AD. EEG signals of AD patients are characterized by a “slowing”, meaning the power increases in low frequencies (delta and theta) and decreases in higher frequency (alpha and beta), compared to normal elderly. The purpose of our study is the computation of the power current density in eight patients, who were diagnosed with MCI at time T0 and mild AD at time T1 (four months later), starting from the brain active source reconstruction. The novelty is that we employed the eLORETA algorithm, unlike the previous studies which used the old version of the algorithm named LORETA. It is also the first longitudinal study which considers such a short time period to explore the evolution of the disease. Five patients out of eight showed an increasing power in delta and theta bands. Seven patients exhibited a lower activation in alpha 1 and beta 2 bands. Finally, six patients revealed a decreased power in alpha 2 and beta 1 bands. These findings are consistent with those reported in literature. On the other hand, the discrepancy of some outcome could be related to a not yet severe stage of the disease. In our opinion, this study could represent a good starting point for more detailed future investigation. Full article
(This article belongs to the Special Issue Advances in Biomedical Signal Processing)
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6 pages, 666 KiB  
Case Report
Cortical Reorganization after Rehabilitation in a Patient with Conduction Aphasia Using High-Density EEG
by Caterina Formica, Simona De Salvo, Katia Micchìa, Fabio La Foresta, Serena Dattola, Nadia Mammone, Francesco Corallo, Adriana Ciavola, Francesca Antonia Arcadi, Silvia Marino, Alessia Bramanti and Lilla Bonanno
Appl. Sci. 2020, 10(15), 5281; https://doi.org/10.3390/app10155281 - 30 Jul 2020
Cited by 5 | Viewed by 2389
Abstract
Conduction aphasia is a language disorder occurred after a left-brain injury. It is characterized by fluent speech production, reading, writing and normal comprehension, while speech repetition is impaired. The aim of this study is to investigate the cortical responses, induced by language activities, [...] Read more.
Conduction aphasia is a language disorder occurred after a left-brain injury. It is characterized by fluent speech production, reading, writing and normal comprehension, while speech repetition is impaired. The aim of this study is to investigate the cortical responses, induced by language activities, in a sub-acute stroke patient affected by conduction aphasia before and after an intensive speech therapy training. The patient was examined by using High-Density Electroencephalogram (HD-EEG) examination, while was performing language tasks. the patient was evaluated at baseline and after two months after rehabilitative treatment. Our results showed that an intensive rehabilitative process, in sub-acute stroke, could be useful for a good outcome of language deficits. HD-EEG results showed that left parieto-temporol-frontal areas were more activated after 2 months of rehabilitation training compared with baseline. Our results provided evidence that an intensive rehabilitation process could contribute to an inter- and intra-hemispheric reorganization. Full article
(This article belongs to the Special Issue Advances in Biomedical Signal Processing)
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