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Special Issue "Electromagnetic Sensors for Biomedical Applications"

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

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Dr. Ruben Specogna
Website
Guest Editor
Polytechnic Department of Engineering and Architecture, Università degli Studi di Udine, 33100 Udine, Italy
Interests: scientific computing; applied and computational electromagnetics; sensors and biosensors; inverse problems and imaging; image processing; topological data analysis
Dr. Antonio Affanni
Website
Guest Editor
Università degli Studi di Udine, Polytechnic Department of Engineering and Architecture, Udine, Italy
Interests: electronic instrumentation; sensors and biosensors; impedance spectroscopy; biosignal processing

Special Issue Information

Dear Colleagues,

Electromagnetic sensors for biomedical applications are receiving increasing attention both in scientific and industrial communities. Substituting existing bulky and expensive instrumentation with smart sensors having a reduced size and lower cost such as micro total analysis systems (µTAS), lab-on-a-chip, or wearable devices is a challenge from the perspective of telemedicine, point of care analyses, and personalized pharmacological treatment.

In this Special Issue, a wide range of topics are covered, including, but are not limited, to:

  • Modeling, characterization and fabrication of electromagnetic biosensors;
  • Electrical impedance spectroscopy;
  • Electrical impedance tomography (EIT);
  • Magnetic induction tomography (MIT);
  • µTAS;
  • Lab-on-a-chip devices;
  • Electromagnetic sensors for blood analysis;
  • Sensors and methods for cells and living tissue electromagnetic characterization;
  • Electroencephalography (EEG) and magnetoencephalography (MEG);
  • Hardware and biosignal processing for electrocardiography (ECG);
  • Sensors and biosignal processing for stress detection;
  • Wearable and flexible sensors;
  • Sensors for well-being in ageing populations (ambient assisted living).

Dr. Ruben Specogna
Dr. Antonio Affanni
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 papers will be 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 2000 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

  • impedance biosensors
  • impedance spectroscopy
  • electrical impedance tomography (EIT), magnetic induction tomography (MIT)
  • lab-on-a-chip
  • electroencephalography (EEG), magnetoencephalography (MEG), electrocardiography (ECG), telemedicine, wearable sensors, µTAS, point of care analysis, electrodes on flexible substrate, sensors for ambient assisted living (AAL)

Published Papers (11 papers)

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Research

Open AccessArticle
Space–Time–Frequency Multi-Sensor Analysis for Motor Cortex Localization Using Magnetoencephalography
Sensors 2020, 20(9), 2706; https://doi.org/10.3390/s20092706 - 09 May 2020
Abstract
Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity [...] Read more.
Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity to localize high frequency oscillations and loses its robustness for induced responses (ill-defined trigger). The drawback of TFM is that it involves independent analysis of signals from a number of frequency bands, and from co-localized sensors. In the present article, a regression-based multi-sensor space–time–frequency analysis (MSA) approach, which integrates co-localized sensors and/or multi-frequency information, is proposed. To estimate task-specific brain activations, MSA uses cross-validated, shifted, multiple Pearson correlation, calculated from the time–frequency transformed brain signal and the binary signal of stimuli. The results are projected from the sensor space onto the cortical surface. To assess MSA performance, the proposed method was compared to the weighted minimum norm estimate (wMNE) source imaging method, in terms of spatial selectivity and robustness against an ill-defined trigger. Magnetoencephalography (MEG) recordings were performed in fourteen subjects during two motor tasks: finger tapping and elbow flexion/extension. In particular, our results show that the MSA approach provides good localization performance when compared to wMNE and statistically significant improvement of robustness against ill-defined trigger. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
Stress Evaluation in Simulated Autonomous and Manual Driving through the Analysis of Skin Potential Response and Electrocardiogram Signals
Sensors 2020, 20(9), 2494; https://doi.org/10.3390/s20092494 - 28 Apr 2020
Abstract
The evaluation of car drivers’ stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive [...] Read more.
The evaluation of car drivers’ stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver’s stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a Supervised Learning Algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
NeuroVAD: Real-Time Voice Activity Detection from Non-Invasive Neuromagnetic Signals
Sensors 2020, 20(8), 2248; https://doi.org/10.3390/s20082248 - 16 Apr 2020
Abstract
Neural speech decoding-driven brain-computer interface (BCI) or speech-BCI is a novel paradigm for exploring communication restoration for locked-in (fully paralyzed but aware) patients. Speech-BCIs aim to map a direct transformation from neural signals to text or speech, which has the potential for a [...] Read more.
Neural speech decoding-driven brain-computer interface (BCI) or speech-BCI is a novel paradigm for exploring communication restoration for locked-in (fully paralyzed but aware) patients. Speech-BCIs aim to map a direct transformation from neural signals to text or speech, which has the potential for a higher communication rate than the current BCIs. Although recent progress has demonstrated the potential of speech-BCIs from either invasive or non-invasive neural signals, the majority of the systems developed so far still assume knowing the onset and offset of the speech utterances within the continuous neural recordings. This lack of real-time voice/speech activity detection (VAD) is a current obstacle for future applications of neural speech decoding wherein BCI users can have a continuous conversation with other speakers. To address this issue, in this study, we attempted to automatically detect the voice/speech activity directly from the neural signals recorded using magnetoencephalography (MEG). First, we classified the whole segments of pre-speech, speech, and post-speech in the neural signals using a support vector machine (SVM). Second, for continuous prediction, we used a long short-term memory-recurrent neural network (LSTM-RNN) to efficiently decode the voice activity at each time point via its sequential pattern-learning mechanism. Experimental results demonstrated the possibility of real-time VAD directly from the non-invasive neural signals with about 88% accuracy. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition
Sensors 2020, 20(7), 2026; https://doi.org/10.3390/s20072026 - 04 Apr 2020
Abstract
This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors [...] Read more.
This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
The Reflectance of Human Skin in the Millimeter-Wave Band
Sensors 2020, 20(5), 1480; https://doi.org/10.3390/s20051480 - 08 Mar 2020
Abstract
The millimeter-wave band is an ideal part of the electromagnetic radiation to diagnose human skin conditions because this radiation interacts only with tissue down to a depth of a millimetre or less over the band range from 30 GHz to 300 GHz. In [...] Read more.
The millimeter-wave band is an ideal part of the electromagnetic radiation to diagnose human skin conditions because this radiation interacts only with tissue down to a depth of a millimetre or less over the band range from 30 GHz to 300 GHz. In this paper, radiometry is used as a non-contact sensor for measuring the human skin reflectance under normal and wet skin conditions. The mean reflectance of the skin of a sample of 50 healthy participants over the (80–100) GHz band was found to be ~0.615 with a standard deviation of ~0.088, and an experimental measurement uncertainty of ±0.005. The thinner skin regions of the back of the hand, the volar forearms and the inner wrist had reflectances 0.068, 0.068 and 0.062 higher than the thicker skin regions of the palm of the hand, the dorsal forearm and the outer wrist skin. Experimental measurements of human skin reflectance in a normal and a wet state on the back of the hand and the palm of the hand regions indicated that the mean differences in the reflectance before and after the application of water were ~0.078 and ~0.152, respectively. These differences were found to be statistically significant as assessed using t-tests (34 paired t-tests and six independent t-tests were performed to assess the significance level of the mean differences in the reflectance of the skin). Radiometric measurements in this paper show the quantitative variations in the skin reflectance between locations, sexes, and individuals. The study reveals that these variations are related to the skin thickness and water content, a capability that has the potential to allow radiometry to be used as a non-contact sensor to detect and monitor skin conditions such as eczema, psoriasis, malignancy, and burn wounds. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
Fall Detection Using Multiple Bioradars and Convolutional Neural Networks
Sensors 2019, 19(24), 5569; https://doi.org/10.3390/s19245569 - 17 Dec 2019
Cited by 1
Abstract
A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages [...] Read more.
A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach
Sensors 2019, 19(20), 4454; https://doi.org/10.3390/s19204454 - 14 Oct 2019
Abstract
Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the [...] Read more.
Simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) are at the forefront of technologies of interest to physicians and scientists because they combine the benefits of both modalities—better time resolution (hdEEG) and space resolution (fMRI). However, EEG measurements in the scanner contain an electromagnetic field that is induced in leads as a result of gradient switching slight head movements and vibrations, and it is corrupted by changes in the measured potential because of the Hall phenomenon. The aim of this study is to design and test a methodology for inspecting hidden EEG structures with respect to artifacts. We propose a top-down strategy to obtain additional information that is not visible in a single recording. The time-domain independent component analysis algorithm was employed to obtain independent components and spatial weights. A nonlinear dimension reduction technique t-distributed stochastic neighbor embedding was used to create low-dimensional space, which was then partitioned using the density-based spatial clustering of applications with noise (DBSCAN). The relationships between the found data structure and the used criteria were investigated. As a result, we were able to extract information from the data structure regarding electrooculographic, electrocardiographic, electromyographic and gradient artifacts. This new methodology could facilitate the identification of artifacts and their residues from simultaneous EEG in fMRI. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems
Sensors 2019, 19(17), 3769; https://doi.org/10.3390/s19173769 - 30 Aug 2019
Cited by 5
Abstract
This paper presents a novel motor imagery (MI) classification algorithm using filter-bank common spatial pattern (FBCSP) features based on MI-relevant channel selection. In contrast to existing channel selection methods based on global CSP features, the proposed algorithm utilizes the Fisher ratio of time [...] Read more.
This paper presents a novel motor imagery (MI) classification algorithm using filter-bank common spatial pattern (FBCSP) features based on MI-relevant channel selection. In contrast to existing channel selection methods based on global CSP features, the proposed algorithm utilizes the Fisher ratio of time domain parameters (TDPs) and correlation coefficients: the channel with the highest Fisher ratio of TDPs, named principle channel, is selected and a supporting channel set for the principle channel that consists of highly correlated channels to the principle channel is generated. The proposed algorithm using the FBCSP features generated from the supporting channel set for the principle channel significantly improved the classification performance. The performance of the proposed method was evaluated using BCI Competition III Dataset IVa (18 channels) and BCI Competition IV Dataset I (59 channels). Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
A Blood Flow Volume Linear Inversion Model Based on Electromagnetic Sensor for Predicting the Rate of Arterial Stenosis
Sensors 2019, 19(13), 3006; https://doi.org/10.3390/s19133006 - 08 Jul 2019
Cited by 1
Abstract
This paper presents a mathematical model of measuring blood flow based on electromagnetic induction for predicting the rate of arterial stenosis. Firstly, an electrode sensor was used to collect the induced potential differences from human skin surface in a uniform magnetic field. Then, [...] Read more.
This paper presents a mathematical model of measuring blood flow based on electromagnetic induction for predicting the rate of arterial stenosis. Firstly, an electrode sensor was used to collect the induced potential differences from human skin surface in a uniform magnetic field. Then, the inversion matrix was constructed by the weight function theory and finite element method. Next, the blood flow volume inversion model was constructed by combining the induction potential differences and inversion matrix. Finally, the rate of arterial stenosis was predicted based on mathematical relationship between blood flow and the area of arterial stenosis. To verify the accuracy of the model, a uniform magnetic field distribution of Helmholtz coil and a 3D geometric model of the ulnar artery of the forearm with different rates of stenosis were established in COMSOL, a finite element analysis software. Simulation results showed that the inversion model had high accuracy in the measurement of blood flow and the prediction of rate of stenosis, and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
Recording of Bipolar Multichannel ECGs by a Smartwatch: Modern ECG Diagnostic 100 Years after Einthoven
Sensors 2019, 19(13), 2894; https://doi.org/10.3390/s19132894 - 30 Jun 2019
Cited by 4
Abstract
Aims: Feasibility study of accurate three lead ECG recording (Einthoven I, II and III) using an Apple Watch Series 4. Methods: In 50 healthy subjects (18 male; age: 40 ± 12 years) without known cardiac disorders, a 12-lead ECG and three bipolar [...] Read more.
Aims: Feasibility study of accurate three lead ECG recording (Einthoven I, II and III) using an Apple Watch Series 4. Methods: In 50 healthy subjects (18 male; age: 40 ± 12 years) without known cardiac disorders, a 12-lead ECG and three bipolar ECGs, corresponding to Einthoven leads I, II and III were recorded using an Apple Watch Series 4. Einthoven I was recorded with the watch on the left wrist and the right index finger on the crown, Einthoven II with the watch on the left lower abdomen and the right index finger on the crown, Einthoven III with the watch on the left lower abdomen and the left index finger on the crown. Four experienced cardiologists were independently asked to assign the watch ECGs to Einthoven leads from 12-lead ECG for each subject. Results: All watch ECGs showed an adequate signal quality with 134 ECGs of good (89%) and 16 of moderate signal quality (11%). Ninety-one percent of all watch ECGs were assigned correctly to corresponding leads from 12-lead ECG. Thirty-nine subjects (78%) were assigned correctly by all cardiologists. All assignment errors occurred in patients with similar morphologies and amplitudes in at least two of the three recorded leads. Erroneous assignment of all watch ECGs to leads from standard ECG occurred in no patient. Conclusion: Recording of Einthoven leads I-III by a smartwatch is accurate and highly comparable to standard ECG. This might contribute to an earlier detection of cardiac disorders, which are associated with repolarization abnormalities or arrhythmias. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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Open AccessArticle
Magnetic Induction Spectroscopy for Biomass Measurement: A Feasibility Study
Sensors 2019, 19(12), 2765; https://doi.org/10.3390/s19122765 - 20 Jun 2019
Cited by 2
Abstract
Background: Biomass measurement and monitoring is a challenge in a number of biotechnology processes where fast, inexpensive, and non-contact measurement techniques would be of great benefit. Magnetic induction spectroscopy (MIS) is a novel non-destructive and contactless impedance measurement technique with many potential industrial [...] Read more.
Background: Biomass measurement and monitoring is a challenge in a number of biotechnology processes where fast, inexpensive, and non-contact measurement techniques would be of great benefit. Magnetic induction spectroscopy (MIS) is a novel non-destructive and contactless impedance measurement technique with many potential industrial and biomedical applications. The aim of this paper is to use computer modeling and experimental measurements to prove the suitability of the MIS system developed at the University of South Wales for controlled biomass measurements. Methods: The paper reports experimental measurements conducted on saline solutions and yeast suspensions at different concentrations to test the detection performance of the MIS system. The commercial electromagnetic simulation software CST was used to simulate the measurement outcomes with saline solutions and compare them with those of the actual measurements. We adopted two different ways for yeast suspension preparation to assess the system’s sensitivity and accuracy. Results: For saline solutions, the simulation results agree well with the measurement results, and the MIS system was able to distinguish saline solutions at different concentrations even in the small range of 0–1.6 g/L. For yeast suspensions, regardless of the preparation method, the MIS system can reliably distinguish yeast suspensions with lower concentrations 0–20 g/L. The conductivity spectrum of yeast suspensions present excellent separability between different concentrations and dielectric dispersion property at concentrations higher than 100 g/L. Conclusions: The South Wales MIS system can achieve controlled yeast measurements with high sensitivity and stability, and it shows promising potential applications, with further development, for cell biology research where contactless monitoring of cellular density is of relevance. Full article
(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)
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