Advances in Biosignal Processing and Biomedical Data Analysis, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 25 June 2024 | Viewed by 880

Special Issue Editor


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Guest Editor
IT Research Institute, Chosun University, 309 Pilmun-daero, Dong-gu, Gwang-Ju 61452, Republic of Korea
Interests: biosignal processing; biometrics; pattern recognition; wearable embedded system
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Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue, titled “Advances in Biosignal Processing and Biomedical Data Analysis”.

Biosignals have unique characteristics for each individual and are mainly used for disease judgment, prediction and health status monitoring. As such, they play an important role in diagnosis. Using these characteristics, personal recognition via biosignals has recently been successfully performed. Biosignals are generated inside the body, which is advantageous for security.

Recent progress in machine learning techniques, and in particular deep learning, has revolutionized various fields of artificial vision, significantly pushing state-of-the-art artificial intelligence systems into a wide range of high-level tasks. Such progress can help address problems in applications of biosignal data based on embedded systems.

We invite authors to submit original research articles, review articles and significant preliminary communications covering (but not limited to) the following topics and scopes:

  • Big data processing for biometrics;
  • Biometric feature extraction based on deep learning;
  • Biometrics based on deep learning;
  • Advanced technologies in biosignal processing;
  • Deep learning architecture modeling for biosignals;
  • Analysis and utilization of various biosignals;
  • Biosignal processing based on wearable devices;
  • Architectures of and applications in wearable devices.

Prof. Dr. Sung Bum Pan
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

  • biosignal processing
  • biomedical data analysis
  • deep learning
  • wearable system

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Published Papers (1 paper)

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Research

17 pages, 1548 KiB  
Article
Investigation of the Clinical Effectiveness and Prognostic Factors of Voice Therapy in Voice Disorders: A Pilot Study
by Ji-Yeoun Lee, Ji-Hye Park, Ji-Na Lee and Ah-Ra Jung
Appl. Sci. 2023, 13(20), 11523; https://doi.org/10.3390/app132011523 - 20 Oct 2023
Viewed by 695
Abstract
Examining the relationship between the prognostic factors and the effectiveness of voice therapy is a crucial step in developing personalized treatment strategies for individuals with voice disorders. This study recommends using the multilayer perceptron model (MLP) to comprehensively analyze the prognostic factors, with [...] Read more.
Examining the relationship between the prognostic factors and the effectiveness of voice therapy is a crucial step in developing personalized treatment strategies for individuals with voice disorders. This study recommends using the multilayer perceptron model (MLP) to comprehensively analyze the prognostic factors, with various parameters, including personal habits and acoustic parameters, that can influence the effectiveness of before-and-after voice therapy in individuals with speech disorders. Various methods, including the assessment of personal characteristics, acoustic analysis, statistical analysis, binomial logistic regression analysis, and MLP, are implemented in this experiment. Accuracies of 87.5% and 85.71% are shown for the combination of optimal input parameters for female and male voices, respectively, through the MLP model. This fact validates the selection of input parameters when building our model. Good prognostic indicators for the clinical effectiveness of voice therapy in voice disorders are jitter (post-treatment) for females and MPT (pre-treatment) for males. The results are expected to provide a foundation for modeling research utilizing artificial intelligence in voice therapy for voice disorders. In terms of follow-up studies, it will be necessary to conduct research that utilizes big data to analyze the optimal parameters for predicting the clinical effectiveness of voice disorders. Full article
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