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Signal Processing and Machine Learning Approaches for Processing Biomedical Sensor Signals

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 2692

Special Issue Editors

Prof. Dr. Wing-Kuen Ling

E-Mail Website
Guest Editor
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: biomedical signal denoising; machine learning with applications in biomedical signal classification and regression; nonlinear dynamics with applications in EEG and ECG modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Steve Ling

E-Mail Website
Guest Editor
Department of Electrical and Data Engineering, University of Technology Sydney, Sydney 00099F, Australia
Interests: neural networks; medical imaging; BCI applications and non-invasive bioinstrumentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical sensor signals such as electrocardiograms, photoplethysmograms and electroencephalograms are always affected by noise, meaning that their signal quality is usually very poor. Existing works have focused on the development of denoising algorithms. At present, some advanced signal processing techniques such as wavelet-based denoising methods, empirical mode decomposition-based denoising methods, variational mode decomposition-based denoising methods and singular spectrum analysis-based denoising methods have been developed. In addition, some machine learning approaches such as deep learning approaches have also recently been developed to enable denoising. These techniques can further improve the signal-to-noise ratio of biomedical sensor signals. However, if the signal-to-noise ratio is too low, then the quality of the denoised signals will still be too low for further processing. Hence, some advanced signal processing and machine learning techniques are being developed for the screening of biomedical sensor signals. This call for papers aims to publish articles on novel signal processing and machine learning techniques used for both the denoising and signal screening of biomedical sensor signals.

Prof. Dr. Wing-Kuen Ling
Dr. Steve Ling
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. Sensors 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 2600 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

  • denoising
  • signal screening
  • signal processing
  • machine learning
  • biomedical sensor signals

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Published Papers (4 papers)

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Research

13 pages, 2783 KiB  
Article
Proton Range Measurement Precision in Ionoacoustic Experiments with Wavelet-Based Denoising Algorithm
by Elia Arturo Vallicelli, Andrea Baschirotto, Lorenzo Stevenazzi, Mattia Tambaro and Marcello De Matteis
Sensors 2025, 25(14), 4247; https://doi.org/10.3390/s25144247 - 8 Jul 2025
Viewed by 226
Abstract
This work presents the experimental results of a wavelet transform denoising algorithm (WTDA) that improves the ionoacoustic signal-to-noise ratio (SNR) and proton range measurement precision. Ionoacoustic detectors exploit the ultrasound signal generated by pulsed proton beams in energy absorbers (water or the human [...] Read more.
This work presents the experimental results of a wavelet transform denoising algorithm (WTDA) that improves the ionoacoustic signal-to-noise ratio (SNR) and proton range measurement precision. Ionoacoustic detectors exploit the ultrasound signal generated by pulsed proton beams in energy absorbers (water or the human body) to localize the energy deposition with sub-millimeter precision, with interesting applications in beam monitoring during oncological hadron therapy treatments. By improving SNR and measurement precision, the WTDA allows significant reduction of the extra dose necessary for beam characterization. To validate the WTDA’s performance, two scenarios are presented. First, the WTDA was applied to the ionoacoustic signals from a 20 MeV proton beam, where it allowed for increasing the SNR by 17 dB and improving measurement precision by a factor of two. Then, the WTDA was applied to the simulated signals from a 200 MeV clinical beam where, compared to state-of-the-art algorithms, it achieved a −80% dose reduction when achieving the same 30 μm precision and a six-fold precision improvement for the same 17 Gy dose deposition. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning Approaches for Processing Biomedical Sensor Signals)
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22 pages, 2651 KiB  
Article
Multi-Party Verifiably Collaborative Encryption for Biomedical Signals via Singular Spectrum Analysis-Based Chaotic Filter Bank Networks
by Xiwen Zhang, Jianfeng He and Bingo Wing-Kuen Ling
Sensors 2025, 25(12), 3823; https://doi.org/10.3390/s25123823 - 19 Jun 2025
Viewed by 272
Abstract
This paper proposes a multi-party verifiably collaborative system for encrypting the nonlinear and the non-stationary biomedical signals captured by biomedical sensors via the singular spectrum analysis (SSA)-based chaotic networks. In particular, the raw signals are first decomposed into the multiple components by the [...] Read more.
This paper proposes a multi-party verifiably collaborative system for encrypting the nonlinear and the non-stationary biomedical signals captured by biomedical sensors via the singular spectrum analysis (SSA)-based chaotic networks. In particular, the raw signals are first decomposed into the multiple components by the SSA. Then, these decomposed components are fed into the chaotic filter bank networks for performing the encryption. To perform the multi-party verifiably collaborative encryption, the window length of the SSA and the total number of the layers in the chaotic network are flexibly designed to match the total number of the collaborators. The computer numerical simulation results show that our proposed system achieves a good encryption performance. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning Approaches for Processing Biomedical Sensor Signals)
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15 pages, 329 KiB  
Article
Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform
by Patryk Zych, Kacper Filipek, Agata Mrozek-Czajkowska and Piotr Kuwałek
Sensors 2025, 25(11), 3284; https://doi.org/10.3390/s25113284 - 23 May 2025
Viewed by 505
Abstract
Brain–computer interfaces (BCIs) have garnered significant interest due to their potential to enable communication and control for individuals with limited or no ability to interact with technologies in a conventional way. By applying electrical signals generated by brain cells, BCIs eliminate the need [...] Read more.
Brain–computer interfaces (BCIs) have garnered significant interest due to their potential to enable communication and control for individuals with limited or no ability to interact with technologies in a conventional way. By applying electrical signals generated by brain cells, BCIs eliminate the need for physical interaction with external devices. This study investigates the performance of traditional classifiers—specifically, linear discriminant analysis (LDA) and support vector machines (SVMs)—in comparison with a hybrid neural network model for EEG-based gesture classification. The dataset comprised EEG recordings of seven distinct gestures performed by 33 participants. Binary classification tasks were conducted using both raw windowed EEG signals and features extracted via bandpower and the empirical wavelet transform (EWT). The hybrid neural network architecture demonstrated higher classification accuracy compared to the standard classifiers. These findings suggest that combining featuring extraction with deep learning models offers a promising approach for improving EEG gesture recognition in BCI systems. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning Approaches for Processing Biomedical Sensor Signals)
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22 pages, 3141 KiB  
Article
Estimation of Pressure Pain in the Lower Limbs Using Electrodermal Activity, Tissue Oxygen Saturation, and Heart Rate Variability
by Youngho Kim, Seonggeon Pyo, Seunghee Lee, Changeon Park and Sunghyuk Song
Sensors 2025, 25(3), 680; https://doi.org/10.3390/s25030680 - 23 Jan 2025
Viewed by 1215
Abstract
Quantification of pain or discomfort induced by pressure is essential for understanding human responses to physical stimuli and improving user interfaces. Pain research has been conducted to investigate physiological signals associated with discomfort and pain perception. This study analyzed changes in electrodermal activity [...] Read more.
Quantification of pain or discomfort induced by pressure is essential for understanding human responses to physical stimuli and improving user interfaces. Pain research has been conducted to investigate physiological signals associated with discomfort and pain perception. This study analyzed changes in electrodermal activity (EDA), tissue oxygen saturation (StO2), heart rate variability (HRV), and Visual Analog Scale (VAS) under pressures of 10, 20, and 30 kPa applied for 3 min to the thigh, knee, and calf in a seated position. Twenty participants were tested, and relationships between biosignals, pressure intensity, and pain levels were evaluated using Friedman tests and post-hoc analyses. Multiple linear regression models were used to predict VAS and pressure, and five machine learning models (SVM, Logistic Regression, Random Forest, MLP, KNN) were applied to classify pain levels (no pain: VAS 0, low: VAS 1–3, moderate: VAS 4–6, high: VAS 7–10) and pressure intensity. The results showed that higher pressure intensity and pain levels affected sympathetic nervous system responses and tissue oxygen saturation. Most EDA features and StO2 significantly changed according to pressure intensity and pain levels, while NN interval and HF among HRV features showed significant differences based on pressure intensity or pain level. Regression analysis combining biosignal features achieved a maximum R2 of 0.668 in predicting VAS and pressure intensity. The four-level classification model reached an accuracy of 88.2% for pain levels and 81.3% for pressure intensity. These results demonstrated the potential of EDA, StO2, HRV signals, and combinations of biosignal features for pain quantification and prediction. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning Approaches for Processing Biomedical Sensor Signals)
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