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Applied Sciences
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Current Updates in Clinical Biomedical Signal Processing
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Current Updates in Clinical 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: 20 November 2025 | Viewed by 580

Special Issue Editors

Dr. Khuram Naveed

E-Mail Website
Guest Editor
Department of Dentistry and Oral Health, Aarhus Universitet, Nordre Ringgade 1, 8000 Aarhus, Denmark
Interests: statistical signal processing; signal and image processing; multiscale time–frequency analysis; machine learning and medical diagnosis
Dr. Ruben Pauwels

E-Mail Website
Guest Editor
Department of Dentistry and Oral Health, Aarhus Universitet, Nordre Ringgade 1, 8000 Aarhus, Denmark
Interests: medical imaging; image processing; medical physics; deep learning

Special Issue Information

Dear Colleagues,

Biomedical signal processing is increasingly becoming an integral part of clinical practice owing to the technological advancements in acquisition and processing methods. This Special Issue seeks to showcase the latest updates and innovations in the processing and analysis of biomedical signals. By exploring new techniques and approaches, we aim to highlight improvements in signal acquisition, noise reduction, and data interpretation that contribute to enhanced diagnostic accuracy and patient care. We encourage submissions that explore advancements in signal processing algorithms, machine learning applications, and the integration of new technologies into clinical practice. This Special Issue will provide a broad perspective on current trends and emerging practices in the field, fostering knowledge exchange and collaboration among researchers and practitioners. Our goal is to offer insights into how these advancements are shaping the future of clinical signal processing and contributing to the development of more effective and personalized medical solutions.

Dr. Khuram Naveed
Dr. Ruben Pauwels
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. 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

  • biomedical signal processing
  • advancements in data acquisition
  • clinical investigations and decision making
  • machine learning applications in healthcare
  • noise reduction and signal enhancement methods
  • computer-aided diagnosis
  • personalized medicine
  • innovations in healthcare technology
  • data analysis and inpretation
  • signal processing algorithms

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Published Papers (2 papers)

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Research

20 pages, 5473 KiB  
Article
Dynamic Heart Rate Variability Vector and Premature Ventricular Contractions Patterns in Adult Hemodialysis Patients: A 48 h Risk Exploration
by Gabriel Vega-Martínez, Francisco José Ramos-Becerril, Josefina Gutiérrez-Martínez, Arturo Vera-Hernández, Carlos Alvarado-Serrano and Lorenzo Leija-Salas
Appl. Sci. 2025, 15(9), 5122; https://doi.org/10.3390/app15095122 - 5 May 2025
Abstract
Chronic kidney disease (CKD) is a progressive pathology characterized by gradual function loss. It is accompanied by complications including cardiovascular disorders. This study involves 4 -h electrocardiographic records from the Telemetric and Holter ECG Warehouse (THEW) project database to analyze the dynamics in [...] Read more.
Chronic kidney disease (CKD) is a progressive pathology characterized by gradual function loss. It is accompanied by complications including cardiovascular disorders. This study involves 4 -h electrocardiographic records from the Telemetric and Holter ECG Warehouse (THEW) project database to analyze the dynamics in heart rate variability (HRV) indices of 51 patients with CKD. It proposes three algorithms to process long-term electrocardiography records: QRS complex and R-wave detection, premature ventricular contraction (PVC) identification, and tachograms. PVCs were analyzed with the consideration of the changes occurring before, during, and after hemodialysis, especially during the interdialytic period. The hour with the highest PVCs occurrence was identified and used to assess HRV fluctuations and segmented into 5 min blocks with a 0.77 min overlap, yielding a dynamic HRV vector, one for each of seven HRV indices selected to evaluate autonomic nervous system balance. R-wave and PVC identification resulted in 97.53% and 85.83% positive predictive values, respectively. PVCs’ prevalence and HRV changes’ relationship in 48 h records could relate to cardiovascular risk. The stratification of hemodialysis patients into three distinct PVC patterns (p < 0.001) identified two clinically significant high-risk subgroups: Class 1, indicative of electrical instability, and Class 3, of advanced autonomic dysfunction, demonstrating divergent arrhythmogenic mechanisms with direct implications for risk stratification. Full article
(This article belongs to the Special Issue Current Updates in Clinical Biomedical Signal Processing)
19 pages, 1748 KiB  
Article
The Effect of Cut-Off Frequency on Signal Features When Filtering Equine sEMG Signal from Selected Extensor Muscles
by Małgorzata Domino, Marta Borowska, Elżbieta Stefanik, Natalia Domańska-Kruppa and Bernard Turek
Appl. Sci. 2025, 15(9), 4737; https://doi.org/10.3390/app15094737 - 24 Apr 2025
Viewed by 115
Abstract
The use of surface electromyography (sEMG) in equine locomotion research has increased significantly due to the essential role of balanced, symmetrical, and efficient movement in riding. However, variations in sEMG signal processing for forelimb extensor muscles across studies have made cross-study comparisons challenging. [...] Read more.
The use of surface electromyography (sEMG) in equine locomotion research has increased significantly due to the essential role of balanced, symmetrical, and efficient movement in riding. However, variations in sEMG signal processing for forelimb extensor muscles across studies have made cross-study comparisons challenging. This study aims to compare the sEMG signal characteristics from carpal extensor muscles under different filtering methods: raw signal, low-pass filtering (10 Hz cut-off), and bandpass filtering (40–450 Hz cut-off and 7–200 Hz cut-off). sEMG signals were collected from four muscles of three horses during walking and trotting. The raw signals were normalized and filtered separately using a 4th-order Butterworth filter: low-pass 10 Hz, bandpass 40–450 Hz, or bandpass 7–200 Hz. For each filtered signal variant, eight activity bursts were annotated, and amplitude, root mean square (RMS), median frequency (MF), and signal-to-noise ratio (SNR) were extracted. Signal loss and residual signal were calculated to assess noise reduction and data retention. For m. extensor digitorum lateralis and m. extensor carpi ulnaris, bandpass filtering at 40–450 Hz resulted in the lowest signal loss and the highest amplitude, RMS, MF, and SNR after filtering. However, variations were observed for the other two carpal extensors. These findings support the hypotheses that the characteristics of myoelectric activity in equine carpal extensors vary depending on the filtering method applied and differ among individual muscles, thereby guiding future research on sEMG signal processing and, consequently, equine biomechanics. Since both noise and its reduction alter raw sEMG signals, potentially affecting data analysis, this study provides valuable insights for improving the reliability and reproducibility of equine biomechanics research across different sEMG studies. Full article
(This article belongs to the Special Issue Current Updates in Clinical Biomedical Signal Processing)
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