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Signals
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Advances in Biomedical Signal Processing and Analysis
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Advances in Biomedical Signal Processing and Analysis

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 4635

Special Issue Editors

Dr. Arash Gharehbaghi

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Guest Editor
Department of Biomedical Engineering, Linköping University, Linköping, Sweden
Interests: biomedical signal processing; machine learning; statistical pattern recognition
Prof. Dr. Ankica Babic

E-Mail Website
Guest Editor
1. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway
2. Department of Biomedical Engineering, Linköping University, Linköping, Sweden
Interests: medical informatics; data mining; classification; artificial intelligence; machine learning; pattern recognition; clustering

Special Issue Information

Dear Colleagues,

Biomedical signals are key indicators of physiological activity within the human body, providing vital information regarding the health and well-being of the individual. The presence of various sources of distortion unavoidably contaminates the signals, making it challenging to extract diagnostic information from the signal due to the nonstationary and non-ergodic behaviors of physiological signals. The integration of stochastic and ensemble processing of biomedical signals has led to significant advancements in signal understanding, complemented by sophisticated machine learning methodologies for classification and representation. The idea of cyclic stochastic processing of biomedical signals has significantly enriched the processing task by leveraging statistical processing methods. However, it should be noted that this topic is applicable only to the physiological signals in which the cyclic characteristics are intrinsically introduced. The heart sound signal (or alternatively phonocardiogram), lung sound signal, electrocardiogram, and sleep electroencephalogram are well-known signals with cyclic characteristics. The Special Issue welcomes papers addressing the advanced methods for processing biomedical signals. The scope of this issue is sufficiently broad, encompassing various study objectives, including denoising, representation, optimization, and classification of biomedical signals. The integration of advanced deep learning methods with processing techniques for cyclic biomedical signals, such as heart sound signals, is particularly encouraged.

Dr. Arash Gharehbaghi
Prof. Dr. Ankica Babic
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 250 words) can be sent to the Editorial Office for assessment.

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. Signals 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 1200 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

  • cyclic biomedical signals
  • heart sound
  • lung sound
  • electroencephalogram (EEG)
  • electrocardiogram (ECG)

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

19 pages, 463 KB  
Article
Evaluating the Performance of eGeMAPS Features in Detecting Depression Using Resampling Methods
by Joshua Turnipseed and Benedito J. B. Fonseca, Jr.
Signals 2026, 7(3), 41; https://doi.org/10.3390/signals7030041 - 6 May 2026
Viewed by 167
Abstract
This paper investigates how well eGeMAPS features can be used to classify depression from a patient’s speech audio samples through the use of statistical resampling methods. We use permutation tests to evaluate, with high confidence, whether eGeMAPS features and the speaker’s depression status [...] Read more.
This paper investigates how well eGeMAPS features can be used to classify depression from a patient’s speech audio samples through the use of statistical resampling methods. We use permutation tests to evaluate, with high confidence, whether eGeMAPS features and the speaker’s depression status are dependent. We use bootstrap confidence intervals to test, with high confidence, whether eGeMAPS features are able to better discriminate depression in male speakers than in female speakers. Lastly, we compare the detection power of different subsets of the eGeMAPS features. We use an open-source dataset of depressed and non-depressed speakers (E-DAIC), an open-source audio feature extractor (eGeMAPS), and open-source machine learning classifiers (WEKA) to enable replication of results and establish a baseline for future studies. Full article
(This article belongs to the Special Issue Advances in Biomedical Signal Processing and Analysis)
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18 pages, 3536 KB  
Article
Standardizing EMG Pipelines for Muscle Synergy Analysis: A Large-Scale Evaluation of Filtering, Normalization and Criteria
by Kunkun Zhao, Yaowei Jin, Yizhou Feng, Jianqing Li and Yuxuan Zhou
Signals 2025, 6(4), 68; https://doi.org/10.3390/signals6040068 - 1 Dec 2025
Cited by 1 | Viewed by 2048
Abstract
Muscle synergies offer valuable insights into the movement strategies employed by the central nervous system and present a promising avenue for clinical applications. However, the field lacks a complete understanding of how surface electromyography processing parameters affect muscle synergy analysis, which in turn [...] Read more.
Muscle synergies offer valuable insights into the movement strategies employed by the central nervous system and present a promising avenue for clinical applications. However, the field lacks a complete understanding of how surface electromyography processing parameters affect muscle synergy analysis, which in turn has hindered cross-study comparisons and the translation of experimental results to clinical contexts. To address the gap, this study presents a systematic evaluation of interactive effects of three key parameters on muscle synergy analysis, including nine cut-off frequencies of low-pass filters, five normalization methods, and five synergy extraction criteria, covering 225 unique combinations. Using a comprehensive running dataset of 135 subjects, this study examined variance accounted for (VAF) and correlation coefficient (R2) metrics, the number of synergies, and synergy structure consistency under different parameter settings. Synergy similarity was used as a quantitative measure of synergy stability across different parameter settings. The results demonstrated that cut-off frequencies, normalization methods, and criteria choices interactively influenced the outcomes. Notably, VAF consistently yielded higher values than R2, highlighting differences in how these metrics capture explained variance. Error VAF (EVAF) emerged as the most robust criterion for determining the number of synergies, especially when combined with normalization methods by maximum value (MAX), average value (AVE), or unit variance (UVA) and moderately high cut-off frequencies, which led to more stable synergy structures across conditions. Furthermore, the predefined threshold associated with each criterion markedly affected the estimated number of synergies. These findings provide structured guidelines for muscle synergy analysis, helping to standardize preprocessing and extraction parameters, improve reproducibility across studies, and enhance the clinical applicability of synergy-based assessments. Full article
(This article belongs to the Special Issue Advances in Biomedical Signal Processing and Analysis)
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18 pages, 1949 KB  
Article
EEG-Based Analysis of Motor Imagery and Multi-Speed Passive Pedaling: Implications for Brain–Computer Interfaces
by Cristian Felipe Blanco-Diaz, Aura Ximena Gonzalez-Cely, Denis Delisle-Rodriguez and Teodiano Freire Bastos-Filho
Signals 2025, 6(4), 52; https://doi.org/10.3390/signals6040052 - 1 Oct 2025
Viewed by 1594
Abstract
Decoding motor imagery (MI) of lower-limb movements from electroencephalography (EEG) signals remains a challenge due to the involvement of deep cortical regions, limiting the applicability of Brain–Computer Interfaces (BCIs). This study proposes a novel protocol that combines passive pedaling (PP) as sensory priming [...] Read more.
Decoding motor imagery (MI) of lower-limb movements from electroencephalography (EEG) signals remains a challenge due to the involvement of deep cortical regions, limiting the applicability of Brain–Computer Interfaces (BCIs). This study proposes a novel protocol that combines passive pedaling (PP) as sensory priming with MI at different speeds (30, 45, and 60 rpm) to improve EEG-based classification. Ten healthy participants performed PP followed by MI tasks while EEG data were recorded. An increase in spectral relative power around Cz associated with both PP and MI was observed, varying with speed and suggesting that PP may enhance cortical engagement during MI. Furthermore, our classification strategy, based on Convolutional Neural Networks (CNNs), achieved an accuracy of 0.87–0.89 across four classes (three speeds and rest). This performance was also compared with the standard Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA), which achieved an accuracy of 0.67–0.76. These results demonstrate the feasibility of multiclass decoding of imagined pedaling velocities and lay the groundwork for speed-adaptive BCIs, supporting future personalized and user-centered neurorehabilitation interventions. Full article
(This article belongs to the Special Issue Advances in Biomedical Signal Processing and Analysis)
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