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Advanced Sensors and Sensing Technologies in Biomedical Signal Acquisition and Processing

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 5067

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Special Issue Editors

Dr. Pamela Zontone

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Guest Editor

Polytechnic Department of Engineering and Architecture, University of Udine, Via Delle Scienze 206, 33100 Udine, Italy
Interests: multidimensional signal processing; biophysical signal processing; machine learning

Prof. Dr. Antonio Affanni

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Guest Editor

Polytechnic Department of Engineering and Architecture, University of Udine, Via Delle Scienze 206, 33100 Udine, Italy
Interests: wearable sensors; industrial sensors; lab-on-chip
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Special Issue Information

Dear Colleagues,

Advanced sensing technology is becoming crucial in many medical devices, including wearable devices. The widespread presence of these biomedical sensors has resulted in a huge quantity of recorded data, and the opportunities for new applications, particularly in the field of healthcare and biometrics, are increasing. Machine learning and deep learning techniques, among others, have been shown to be extremely successful in these types of applications, including, for example, those detecting abnormal heart activity, monitoring blood oxygen levels, or analyzing body acceleration and movement. The analysis of physiological signals as a diagnostic tool has become, therefore, very valuable in several research areas, as it helps improve quality of life as well as helps assess the health of the population. The goal of this Special Issue is to highlight the recent advances in sensors and sensing technologies for physiological signal recordings, and all of the signal processing methods that can be applied to these kinds of signals (such as electrocardiogram, electromyogram, electrodermal activity signals, etc.). Original papers that present new research on these topics are welcome. Potential topics include, but are not limited to:

Novel wearable sensor design;
Advances in medical instrumentation;
Flexible sensors;
Novel electrode design for biomedical signal acquisition;
Machine learning and deep learning techniques applied to biomedical signals;
Electroencephalography (EEG);
Hardware and biomedical signal processing for electrocardiography (ECG);
Sensors and biomedical signal processing for stress detection;
Sensors for well-being in aging populations (ambient assisted living);
Sensors for athletic performance improvement.

We look forward to your participation in this Special Issue.

Dr. Pamela Zontone
Prof. Dr. Antonio Affanni
Guest Editors

Manuscript Submission Information

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Keywords

advanced sensors
wearables
biomedical signal processing
biomedical imaging
physiological time-series analysis
machine learning
deep learning
healthcare
disease recognition
risk factor assessment

Published Papers (5 papers)

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Research

15 pages, 1333 KiB

Open AccessArticle

Improving Eye-Tracking Data Quality: A Framework for Reproducible Evaluation of Detection Algorithms

by Christopher Gundler, Matthias Temmen, Alessandro Gulberti, Monika Pötter-Nerger and Frank Ückert

Sensors 2024, 24(9), 2688; https://doi.org/10.3390/s24092688 - 24 Apr 2024

Viewed by 563

Abstract

High-quality eye-tracking data are crucial in behavioral sciences and medicine. Even with a solid understanding of the literature, selecting the most suitable algorithm for a specific research project poses a challenge. Empowering applied researchers to choose the best-fitting detector for their research needs is the primary contribution of this paper. We developed a framework to systematically assess and compare the effectiveness of 13 state-of-the-art algorithms through a unified application interface. Hence, we more than double the number of algorithms that are currently usable within a single software package and allow researchers to identify the best-suited algorithm for a given scientific setup. Our framework validation on retrospective data underscores its suitability for algorithm selection. Through a detailed and reproducible step-by-step workflow, we hope to contribute towards significantly improved data quality in scientific experiments. Full article

(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies in Biomedical Signal Acquisition and Processing)

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9 pages, 323 KiB

Open AccessCommunication

Mechanistic Assessment of Cardiovascular State Informed by Vibroacoustic Sensors

by Ali Zare, Emily Wittrup and Kayvan Najarian

Sensors 2024, 24(7), 2189; https://doi.org/10.3390/s24072189 - 29 Mar 2024

Viewed by 540

Abstract

Monitoring blood pressure, a parameter closely related to cardiovascular activity, can help predict imminent cardiovascular events. In this paper, a novel method is proposed to customize an existing mechanistic model of the cardiovascular system through feature extraction from cardiopulmonary acoustic signals to estimate blood pressure using artificial intelligence. As various factors, such as drug consumption, can alter the biomechanical properties of the cardiovascular system, the proposed method seeks to personalize the mechanistic model using information extracted from vibroacoustic sensors. Simulation results for the proposed approach are evaluated by calculating the error in blood pressure estimates compared to ground truth arterial line measurements, with the results showing promise for this method. Full article

(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies in Biomedical Signal Acquisition and Processing)

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19 pages, 1902 KiB

Open AccessArticle

A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography

by Palpolage Don Shehan Hiroshan Gunawardane, Raymond Robert MacNeil, Leo Zhao, James Theodore Enns, Clarence Wilfred de Silva and Mu Chiao

Sensors 2024, 24(2), 540; https://doi.org/10.3390/s24020540 - 15 Jan 2024

Viewed by 897

Abstract

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human–computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods. Full article

(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies in Biomedical Signal Acquisition and Processing)

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17 pages, 2109 KiB

Open AccessArticle

A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal

by Xintong Shi, Natsuho Niida, Kohei Yamamoto, Tomoaki Ohtsuki, Yutaka Matsui and Kazunari Owada

Sensors 2023, 23(24), 9698; https://doi.org/10.3390/s23249698 - 8 Dec 2023

Viewed by 844

Abstract

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness. Full article

(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies in Biomedical Signal Acquisition and Processing)

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13 pages, 2643 KiB

Open AccessArticle

Unraveling Transformative Effects after tDCS and BCI Intervention in Chronic Post-Stroke Patient Rehabilitation—An Alternative Treatment Design Study

by Jéssica P. S. Lima, Leticia A. Silva, Denis Delisle-Rodriguez, Vivianne F. Cardoso, Ester M. Nakamura-Palacios and Teodiano F. Bastos-Filho

Sensors 2023, 23(23), 9302; https://doi.org/10.3390/s23239302 - 21 Nov 2023

Viewed by 1652

Abstract

Stroke is a debilitating clinical condition resulting from a brain infarction or hemorrhage that poses significant challenges for motor function restoration. Previous studies have shown the potential of applying transcranial direct current stimulation (tDCS) to improve neuroplasticity in patients with neurological diseases or disorders. By modulating the cortical excitability, tDCS can enhance the effects of conventional therapies. While upper-limb recovery has been extensively studied, research on lower limbs is still limited, despite their important role in locomotion, independence, and good quality of life. As the life and social costs due to neuromuscular disability are significant, the relatively low cost, safety, and portability of tDCS devices, combined with low-cost robotic systems, can optimize therapy and reduce rehabilitation costs, increasing access to cutting-edge technologies for neuromuscular rehabilitation. This study explores a novel approach by utilizing the following processes in sequence: tDCS, a motor imagery (MI)-based brain-computer interface (BCI) with virtual reality (VR), and a motorized pedal end-effector. These are applied to enhance the brain plasticity and accelerate the motor recovery of post-stroke patients. The results are particularly relevant for post-stroke patients with severe lower-limb impairments, as the system proposed here provides motor training in a real-time closed-loop design, promoting cortical excitability around the foot area (Cz) while the patient directly commands with his/her brain signals the motorized pedal. This strategy has the potential to significantly improve rehabilitation outcomes. The study design follows an alternating treatment design (ATD), which involves a double-blind approach to measure improvements in both physical function and brain activity in post-stroke patients. The results indicate positive trends in the motor function, coordination, and speed of the affected limb, as well as sensory improvements. The analysis of event-related desynchronization (ERD) from EEG signals reveals significant modulations in Mu, low beta, and high beta rhythms. Although this study does not provide conclusive evidence for the superiority of adjuvant mental practice training over conventional therapy alone, it highlights the need for larger-scale investigations. Full article

(This article belongs to the Special Issue Advanced Sensors and Sensing Technologies in Biomedical Signal Acquisition and Processing)

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