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Multiple Sensor Signal and Image Processing for Clinical Application
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Multiple Sensor Signal and Image Processing for Clinical Application

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 7339

Special Issue Editors

Dr. Marios Antonakakis

E-Mail Website
Guest Editor
Kounoupidiana Campus, DISPLAY Laboratory, School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Crete, Greece
Interests: remote sensing; imaging for object detection; deep neuronal networks; machine learning; surveillance systems; non-invasive diagnostic and therapeutic tools; brain connectivity; electromagnetic source imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mihalis Zervakis

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, Technical University of Crete, Khania, Greece
Interests: digital image and signal processing; biomedical applications; remote sensing applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensors for the acquisition of body signals/images (bio-sensors) play a vital role in clinical applications. Single bio-sensors or, recently, multiple bio-sensor acquisitions have revolutionized medical diagnosis, monitoring, and treatment, leading to improved patient outcomes and enhanced healthcare delivery. Digital signals and image processing are a fundamental step towards the accurate clinical application of bio-sensors.

Sensors play a crucial role in collecting various physiological signals and measurements, providing clinicians with valuable information about a patient's health status. From wearable devices to implantable sensors, these technologies enable continuous and remote monitoring of vital signs, bioelectrical activity, biochemical markers, and more. This wealth of data requires advanced signal-processing techniques to extract meaningful information and uncover hidden patterns or abnormalities.

Signal processing techniques enable effective analysis and interpretation of complex medical signals, especially when recorded from multiple sites of the body. Through efficient denoising, filtering, and extracting of relevant features from the signals, clinicians can make accurate, non-invasive diagnoses, monitor treatment efficacy, and detect early warning signs of diseases.

Moreover, image processing techniques have revolutionized medical imaging. From computed tomography (CT) and magnetic resonance imaging (MRI) to ultrasound and optical imaging, these methods enable non-invasive and high-resolution imaging for better disease detection, treatment planning, and intervention guidance.

This Special Issue aims to explore the latest advancements and applications of bio-sensors, signal processing, and image processing in clinical environments. We invite researchers and practitioners to submit their original contributions, including research articles and reviews, focusing on innovative methodologies, algorithms, and systems that enhance clinical decision-making, patient care, and overall healthcare outcomes.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Anika Deng via <anika.deng@mdpi.com> directly.

Dr. Marios Antonakakis
Prof. Dr. Mihalis Zervakis
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.

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

Published Papers (2 papers)

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Research

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17 pages, 3304 KiB  
Article
Evaluation of In-Ear and Fingertip-Based Photoplethysmography Sensors for Measuring Cardiac Vagal Tone Relevant Heart Rate Variability Parameters
by Ankit Parikh, Gwyn Lewis, Hamid GholamHosseini, Usman Rashid, David Rice and Faisal Almesfer
Sensors 2025, 25(5), 1485; https://doi.org/10.3390/s25051485 - 28 Feb 2025
Viewed by 916
Abstract
This paper presents a study undertaken to evaluate the sensor systems that were shortlisted to be used in the development of a portable respiratory-gated transcutaneous auricular vagus nerve stimulation (taVNS) system. To date, all published studies assessing respiratory-gated taVNS have been performed in [...] Read more.
This paper presents a study undertaken to evaluate the sensor systems that were shortlisted to be used in the development of a portable respiratory-gated transcutaneous auricular vagus nerve stimulation (taVNS) system. To date, all published studies assessing respiratory-gated taVNS have been performed in controlled laboratory environments. This limitation arises from the reliance on non-portable sensing equipment, which poses significant logistical challenges. Therefore, we recognised a need to develop a portable sensor system for future research, enabling participants to perform respiratory-gated stimulation conveniently from their homes. This study aimed to measure the accuracy of an in-ear and a fingertip-based photoplethysmography (PPG) sensor in measuring cardiac vagal tone relevant heart rate variability (HRV) parameters of root mean square of successive R-R interval differences (RMSSDs) and the high-frequency (HF) component of HRV. Thirty healthy participants wore the prototype sensor equipment and the gold standard electrocardiogram (ECG) equipment to record beat-to-beat intervals simultaneously during 10 min of normal breathing and 10 min of deep slow breathing (DSB). Additionally, a stretch sensor was evaluated to measure its accuracy in detecting exhalation when compared to the gold standard sensor. We used Bland–Altman analysis to establish the agreement between the prototypes and the ECG system. Intraclass correlation coefficients (ICCs) were calculated to establish consistency between the prototypes and the ECG system. For the stretch sensor, the true positive rate (TPR), false positive rate (FPR), and false negative rate (FNR) were calculated. Results indicate that while ICC values were generally good to excellent, only the fingertip-based sensor had an acceptable level of agreement in measuring RMSSDs during both breathing phases. Only the fingertip-based sensor had an acceptable level of agreement during normal breathing in measuring HF-HRV. The study highlights that a high correlation between sensors does not necessarily translate into a high level of agreement. In the case of the stretch sensor, it had an acceptable level of accuracy with a mean TPR of 85% during normal breathing and 95% during DSB. The results show that the fingertip-based sensor and the stretch sensor had acceptable levels of accuracy for use in the development of the respiratory-gated taVNS system. Full article
(This article belongs to the Special Issue Multiple Sensor Signal and Image Processing for Clinical Application)
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Review

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65 pages, 19918 KiB  
Review
Radiation Detectors and Sensors in Medical Imaging
by Christos Michail, Panagiotis Liaparinos, Nektarios Kalyvas, Ioannis Kandarakis, George Fountos and Ioannis Valais
Sensors 2024, 24(19), 6251; https://doi.org/10.3390/s24196251 - 26 Sep 2024
Cited by 3 | Viewed by 5309
Abstract
Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, [...] Read more.
Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, how the radiation is measured, how the images are formed, and the medical goals they serve. Related to medical goals, detectors fall into two major areas: (i) anatomical imaging, which mainly concerns the techniques of diagnostic radiology, and (ii) functional-molecular imaging, which mainly concerns nuclear medicine. An important parameter in the evaluation of the detectors is the combination of the quality of the diagnostic result they offer and the burden of the patient with radiation dose. The latter has to be minimized; thus, the input signal (radiation photon flux) must be kept at low levels. For this reason, the detective quantum efficiency (DQE), expressing signal-to-noise ratio transfer through an imaging system, is of primary importance. In diagnostic radiology, image quality is better than in nuclear medicine; however, in most cases, the dose is higher. On the other hand, nuclear medicine focuses on the detection of functional findings and not on the accurate spatial determination of anatomical data. Detectors are integrated into projection or tomographic imaging systems and are based on the use of scintillators with optical sensors, photoconductors, or semiconductors. Analysis and modeling of such systems can be performed employing theoretical models developed in the framework of cascaded linear systems analysis (LCSA), as well as within the signal detection theory (SDT) and information theory. Full article
(This article belongs to the Special Issue Multiple Sensor Signal and Image Processing for Clinical Application)
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