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Advances in Biosignal Processing and Biomedical Data Analysis, 2nd Edition
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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: 20 April 2025 | Viewed by 3236

Special Issue Editor

Prof. Dr. Sung Bum Pan

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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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Related Special Issue

Published Papers (3 papers)

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Research

31 pages, 11683 KiB  
Article
Kinematic IMU-Based Assessment of Postural Transitions: A Preliminary Application in Clinical Context
by Cinzia Amici, Joel Pollet, Giorgia Ranica, Roberto Bussola and Riccardo Buraschi
Appl. Sci. 2024, 14(16), 7011; https://doi.org/10.3390/app14167011 - 9 Aug 2024
Viewed by 661
Abstract
This study aims to develop a new methodology for assessing postural transitions, such as sit-to-stand movements, and to preliminarily apply it in a clinical setting. These movements provide valuable information about the state of movement effector system components, whether musculoskeletal, nervous, or cognitive, [...] Read more.
This study aims to develop a new methodology for assessing postural transitions, such as sit-to-stand movements, and to preliminarily apply it in a clinical setting. These movements provide valuable information about the state of movement effector system components, whether musculoskeletal, nervous, or cognitive, and their evaluation is a key point in the functional assessment in the clinical setting of patients with complex rehabilitative needs. The objective of this study was developed by pursuing three goals: verifying the ability to discriminate between healthy and pathological subjects, defining a set of parameters for movement assessment, and thus designing a preliminary evaluation paradigm for future clinical applications. We investigated the signals from a single IMU sensor applied to subjects (20 healthy and 13 patients) performing five different postural transitions. A set of six kinematic variables that allowed a quantitative assessment of motion was identified, namely total time, smoothness, fluency, velocity, jerk root mean square, and maximum jerk variation. At the end of the study, the adopted methodology and set of parameters were shown to be able to quantitatively assess postural transitions in a clinical context and to be able to distinguish healthy subjects from pathological subjects. This, together with future studies, will provide researchers and clinicians with a valuable resource for evaluating the results of a rehabilitation program, as well as for keeping track of patients’ functional status in follow-up evaluations. Full article
(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis, 2nd Edition)
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19 pages, 4827 KiB  
Article
Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism
by Tingwei Wu, Zhaohan Huang, Shilong Li, Qijun Zhao and Fan Pan
Appl. Sci. 2024, 14(15), 6825; https://doi.org/10.3390/app14156825 - 5 Aug 2024
Viewed by 843
Abstract
Heart murmurs play a critical role in assessing the condition of the heart. Murmur quality reflects the subjective human perception of heart murmurs and is an important characteristic strongly linked to cardiovascular diseases (CVDs). This study aims to use deep neural networks to [...] Read more.
Heart murmurs play a critical role in assessing the condition of the heart. Murmur quality reflects the subjective human perception of heart murmurs and is an important characteristic strongly linked to cardiovascular diseases (CVDs). This study aims to use deep neural networks to classify the patients’ murmur quality (i.e., harsh and blowing) from phonocardiogram (PCG) signals. The phonocardiogram recordings with murmurs used for this task are from the CirCor DigiScope Phonocardiogram dataset, which provides the murmur quality labels. The recordings were segmented, and a dataset of 1266 segments with average lengths of 4.1 s from 164 patients’ recordings was obtained. Each patient usually has multiple segments. A deep neural network model based on convolutional neural networks (CNNs) with channel attention and gated recurrent unit (GRU) networks was first used to extract features from the log-Mel spectrograms of segments. Then, the features of different segments from one patient were weighted by the proposed “Feature Attention” module based on the attention mechanism. The “Feature Attention” module contains a layer of global pooling and two fully connected layers. Through it, the different features can learn their weight, which can help the deep learning model distinguish the importance of different features of one patient. Finally, the detection results were produced. The cross-entropy loss function was used to train the model, and five-fold cross-validation was employed to evaluate the performance of the proposed methods. The accuracy of detecting the quality of patients’ murmurs is 73.6%. The F1-scores (precision and recall) for the murmurs of harsh and blowing are 76.8% (73.0%, 83.0%) and 67.8% (76.0%, 63.3%), respectively. The proposed methods have been thoroughly evaluated and have the potential to assist physicians with the diagnosis of cardiovascular diseases as well as explore the relationship between murmur quality and cardiovascular diseases in depth. Full article
(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis, 2nd Edition)
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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 1298
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
(This article belongs to the Special Issue Advances in Biosignal Processing and Biomedical Data Analysis, 2nd Edition)
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