Sensors

Research

26 pages, 10291 KiB

Open AccessArticle

Towards Estimating Arterial Diameter Using Bioimpedance Spectroscopy: A Computational Simulation and Tissue Phantom Analysis

by Yang Yu, Gautam Anand, Andrew Lowe, Huiyang Zhang and Anubha Kalra

Sensors 2022, 22(13), 4736; https://doi.org/10.3390/s22134736 - 23 Jun 2022

Cited by 12 | Viewed by 3650

Abstract

This paper improves the accuracy of quantification in the arterial diameter-dependent impedance variance by altering the electrode configuration. The finite element analysis was implemented with a 3D human wrist fragment using ANSYS Electronics Desktop, containing fat, muscle, and a blood-filled radial artery. Then, [...] Read more.

This paper improves the accuracy of quantification in the arterial diameter-dependent impedance variance by altering the electrode configuration. The finite element analysis was implemented with a 3D human wrist fragment using ANSYS Electronics Desktop, containing fat, muscle, and a blood-filled radial artery. Then, the skin layer and bones were stepwise added, helping to understand the dielectric response of multi-tissues and blood flow from 1 kHz to 1 MHz, the current distribution throughout the wrist, and the optimisation of electrode configurations for arterial pulse sensing. Moreover, a low-cost wrist phantom was fabricated, containing two components: the surrounding tissue simulant (20 wt % gelatine power and 0.017 M sodium chloride (NaCl) solution) and the blood simulant (0.08 M NaCl solution). The blood-filled artery was constricted using a desktop injection pump, and the impedance change was measured by the Multi-frequency Impedance Analyser (MFIA). The simulation revealed the promising capabilities of band electrodes to generate a more uniform current distribution than the traditional spot electrodes. Both simulation and phantom experimental results indicated that a longer spacing between current-carrying (CC) electrodes with shorter spacing between pick-up (PU) electrodes in the middle could sense a more uniform electric field, engendering a more accurate arterial diameter estimation. This work provided an improved electrode configuration for more accurate arterial diameter estimation from the numerical simulation and tissue phantom perspectives. Full article

(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)

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14 pages, 3645 KiB

Open AccessArticle

Investigating Electrical Impedance Spectroscopy for Estimating Blood Flow-Induced Variations in Human Forearm

by Gautam Anand and Andrew Lowe

Sensors 2020, 20(18), 5333; https://doi.org/10.3390/s20185333 - 17 Sep 2020

Cited by 12 | Viewed by 3597

Abstract

This work aims to investigate the feasibility of employing multi-frequency bioimpedance analysis for hemodynamic assessment. Towards this, we aim to explore one of its implementations, electrical impedance spectroscopy (EIS), for estimating changes in radial artery diameter due to blood flow. Following from our [...] Read more.

This work aims to investigate the feasibility of employing multi-frequency bioimpedance analysis for hemodynamic assessment. Towards this, we aim to explore one of its implementations, electrical impedance spectroscopy (EIS), for estimating changes in radial artery diameter due to blood flow. Following from our previous investigations, here, we use a commercial device—the Quadra^® Impedance Spectroscopy device—for impedance measurements of the forearm of three subjects under normal conditions and occluding the artery with a cuff. This was performed simultaneously with ultrasound measurements as a reference. The impedance spectra were measured over time, yielding waveforms reflecting changes due to blood flow. Contributions from the fat/muscle domains were accounted for using the occluded impedance response, resulting in arterial impedance. A modified relationship was approximated to calculate the diameter from the arterial impedance, which showed a similarity with ultrasound measurements. Comparison with the ultrasound measurements revealed differences in phase and amplitude, primarily due to the approximated relationship between impedance and diameter and neglecting the impedance phase analysis. This work shows the potential of EIS, with improvements, towards estimating blood flow-induced variation in arteries. Further analysis and improvements could help place this technology in mainstream clinical practice for hemodynamic monitoring. Full article

(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)

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15 pages, 1774 KiB

Open AccessArticle

Evaluation of Swallowing Related Muscle Activity by Means of Concentric Ring Electrodes

by Javier Garcia-Casado, Gema Prats-Boluda, Yiyao Ye-Lin, Sebastián Restrepo-Agudelo, Estefanía Perez-Giraldo and Andrés Orozco-Duque

Sensors 2020, 20(18), 5267; https://doi.org/10.3390/s20185267 - 15 Sep 2020

Cited by 14 | Viewed by 3542

Abstract

Surface electromyography (sEMG) can be helpful for evaluating swallowing related muscle activity. Conventional recordings with disc electrodes suffer from significant crosstalk from adjacent muscles and electrode-to-muscle fiber orientation problems, while concentric ring electrodes (CREs) offer enhanced spatial selectivity and axial isotropy. The aim [...] Read more.

Surface electromyography (sEMG) can be helpful for evaluating swallowing related muscle activity. Conventional recordings with disc electrodes suffer from significant crosstalk from adjacent muscles and electrode-to-muscle fiber orientation problems, while concentric ring electrodes (CREs) offer enhanced spatial selectivity and axial isotropy. The aim of this work was to evaluate CRE performance in sEMG recordings of the swallowing muscles. Bipolar recordings were taken from 21 healthy young volunteers when swallowing saliva, water and yogurt, first with a conventional disc and then with a CRE. The signals were characterized by the root-mean-square amplitude, signal-to-noise ratio, myopulse, zero-crossings, median frequency, bandwidth and bilateral muscle cross-correlations. The results showed that CREs have advantages in the sEMG analysis of swallowing muscles, including enhanced spatial selectivity and the associated reduction in crosstalk, the ability to pick up a wider range of EMG frequency components and easier electrode placement thanks to its radial symmetry. However, technical changes are recommended in the future to ensure that the lower CRE signal amplitude does not significantly affect its quality. CREs show great potential for improving the clinical monitoring and evaluation of swallowing muscle activity. Future work on pathological subjects will assess the possible advantages of CREs in dysphagia monitoring and diagnosis. Full article

(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)

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21 pages, 8842 KiB

Open AccessArticle

Application of Linear Gradient Magnetic Field in Arterial Profile Scanning Imaging

by Yanjun Liu, Guoqiang Liu, Dan Yang and Bin Xu

Sensors 2020, 20(16), 4547; https://doi.org/10.3390/s20164547 - 13 Aug 2020

Cited by 5 | Viewed by 2753

Abstract

Background and Objectives: Cardiovascular and cerebrovascular diseases caused by arterial stenosis and sclerosis are the main causes of human death. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early disease prediction and monitoring due to their [...] Read more.

Background and Objectives: Cardiovascular and cerebrovascular diseases caused by arterial stenosis and sclerosis are the main causes of human death. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early disease prediction and monitoring due to their high cost and complex operation. The purpose of this paper is to study the coupling effect of arterial blood flow and linear gradient magnetic field, and to propose a method for the reconstruction of the arterial profile, which will lay a theoretical foundation for new electromagnetic artery scanning imaging technology. Methods and Models: A combination coil composed of gradient coils and drive coils is applied as a magnetic field excitation source. By controlling the excitation current, a linearly gradient magnetic field with a line-shaped zero magnetic field is generated, and the zero magnetic field is driven to scan in a specific direction. According to the magnetoelectric effect of blood flow, under the action of the external magnetic field, the voltage signals on the body surface can be detected by measuring electrodes. The location of the artery center line can be determined by the time–space relationship between voltage signals and zero magnetic field scanning. In addition, based on the reciprocity theorem integral equation, a numerical model between the amplitude of the voltage signal and the arterial radius is derived to reconstruct the arterial radius. The above physical process was simulated in the finite element analysis software COMSOL, and the voltage signals obtained from the simulation verified the arterial profile reconstruction. Results: Through finite element simulation verification, the imaging method based on a linear gradient magnetic field has a numerical accuracy of 90% and a spatial resolution of 1 mm. Moreover, under 100 Hz low-frequency alternating current excitation, the single scanning time is 0.005 s, which is far shorter than the arterial blood flow change cycle, meeting the requirements of real-time imaging. The results demonstrate the effectiveness and high theoretical feasibility of the proposed method in real-time arterial imaging. Conclusions: This study indicates the potential application of linear gradient magnetic fields in arterial profile imaging. Compared with traditional electromagnetic imaging methods, the proposed method has the advantages of fast imaging speed and high resolution, showing the certain application value in early real-time imaging of arterial disease. However, further studies are necessary to confirm its effectiveness in clinical practice by more medical data and real cases. Full article

(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)

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20 pages, 1729 KiB

Open AccessArticle

Simultaneous Coercivity and Size Determination of Magnetic Nanoparticles

by Annelies Coene and Jonathan Leliaert

Sensors 2020, 20(14), 3882; https://doi.org/10.3390/s20143882 - 12 Jul 2020

Cited by 9 | Viewed by 4441

Abstract

Magnetic nanoparticles are increasingly employed in biomedical applications such as disease detection and tumor treatment. To ensure a safe and efficient operation of these applications, a noninvasive and accurate characterization of the particles is required. In this work, a magnetic characterization technique is [...] Read more.

Magnetic nanoparticles are increasingly employed in biomedical applications such as disease detection and tumor treatment. To ensure a safe and efficient operation of these applications, a noninvasive and accurate characterization of the particles is required. In this work, a magnetic characterization technique is presented in which the particles are excited by specific pulsed time-varying magnetic fields. This way, we can selectively excite nanoparticles of a given size so that the resulting measurement gives direct information on the size distribution without the need for any a priori assumptions or complex postprocessing procedures to decompose the measurement signal. This contrasts state-of-the-art magnetic characterization techniques. The possibility to selectively excite certain particle types opens up perspectives in “multicolor” particle imaging, where different particle types need to be imaged independently within one sample. Moreover, the presented methodology allows one to simultaneously determine the size-dependent coercivity of the particles. This is not only a valuable structure–property relation from a fundamental point of view, it is also practically relevant to optimize applications like magnetic particle hyperthermia. We numerically demonstrate that the novel characterization technique can accurately reconstruct several particle size distributions and is able to retrieve the coercivity–size relation of the particles. The developed technique advances current magnetic nanoparticle characterization possibilities and opens up exciting pathways for biomedical applications and particle imaging procedures. Full article

(This article belongs to the Special Issue Electromagnetic Sensors for Biomedical Applications)

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16 pages, 3149 KiB

Open AccessArticle

Space–Time–Frequency Multi-Sensor Analysis for Motor Cortex Localization Using Magnetoencephalography

by Vincent Auboiroux, Christelle Larzabal, Lilia Langar, Victor Rohu, Ales Mishchenko, Nana Arizumi, Etienne Labyt, Alim-Louis Benabid and Tetiana Aksenova

Sensors 2020, 20(9), 2706; https://doi.org/10.3390/s20092706 - 9 May 2020

Cited by 4 | Viewed by 3058

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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19 pages, 1740 KiB

Open AccessArticle

Stress Evaluation in Simulated Autonomous and Manual Driving through the Analysis of Skin Potential Response and Electrocardiogram Signals

by Pamela Zontone, Antonio Affanni, Riccardo Bernardini, Leonida Del Linz, Alessandro Piras and Roberto Rinaldo

Sensors 2020, 20(9), 2494; https://doi.org/10.3390/s20092494 - 28 Apr 2020

Cited by 22 | Viewed by 4724

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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13 pages, 4166 KiB

Open AccessArticle

NeuroVAD: Real-Time Voice Activity Detection from Non-Invasive Neuromagnetic Signals

by Debadatta Dash, Paul Ferrari, Satwik Dutta and Jun Wang

Sensors 2020, 20(8), 2248; https://doi.org/10.3390/s20082248 - 16 Apr 2020

Cited by 19 | Viewed by 6265

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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20 pages, 7013 KiB

Open AccessArticle

Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition

by Antonio Affanni

Sensors 2020, 20(7), 2026; https://doi.org/10.3390/s20072026 - 4 Apr 2020

Cited by 35 | Viewed by 7381

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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22 pages, 3254 KiB

Open AccessArticle

The Reflectance of Human Skin in the Millimeter-Wave Band

by Amani Yousef Owda, Neil Salmon, Alexander J Casson and Majdi Owda

Sensors 2020, 20(5), 1480; https://doi.org/10.3390/s20051480 - 8 Mar 2020

Cited by 27 | Viewed by 10228

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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13 pages, 3443 KiB

Open AccessArticle

Fall Detection Using Multiple Bioradars and Convolutional Neural Networks

by Lesya Anishchenko, Andrey Zhuravlev and Margarita Chizh

Sensors 2019, 19(24), 5569; https://doi.org/10.3390/s19245569 - 17 Dec 2019

Cited by 36 | Viewed by 4357

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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21 pages, 2800 KiB

Open AccessArticle

Artifacts in Simultaneous hdEEG/fMRI Imaging: A Nonlinear Dimensionality Reduction Approach

by Marek Piorecky, Vlastimil Koudelka, Jan Strobl, Martin Brunovsky and Vladimir Krajca

Sensors 2019, 19(20), 4454; https://doi.org/10.3390/s19204454 - 14 Oct 2019

Cited by 8 | Viewed by 3902

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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9 pages, 410 KiB

Open AccessArticle

Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems

by Yongkoo Park and Wonzoo Chung

Sensors 2019, 19(17), 3769; https://doi.org/10.3390/s19173769 - 30 Aug 2019

Cited by 30 | Viewed by 3825

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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13 pages, 8641 KiB

Open AccessArticle

A Blood Flow Volume Linear Inversion Model Based on Electromagnetic Sensor for Predicting the Rate of Arterial Stenosis

by Dan Yang, Yan-jun Liu, Bin Xu and Yun-hui Duo

Sensors 2019, 19(13), 3006; https://doi.org/10.3390/s19133006 - 8 Jul 2019

Cited by 7 | Viewed by 3904

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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9 pages, 3111 KiB

Open AccessArticle

Recording of Bipolar Multichannel ECGs by a Smartwatch: Modern ECG Diagnostic 100 Years after Einthoven

by Alexander Samol, Kristina Bischof, Blerim Luani, Dan Pascut, Marcus Wiemer and Sven Kaese

Sensors 2019, 19(13), 2894; https://doi.org/10.3390/s19132894 - 30 Jun 2019

Cited by 35 | Viewed by 6039

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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13 pages, 3517 KiB

Open AccessArticle

Magnetic Induction Spectroscopy for Biomass Measurement: A Feasibility Study

by Ziyi Zhang, Mohammed Ali Roula and Richard Dinsdale

Sensors 2019, 19(12), 2765; https://doi.org/10.3390/s19122765 - 20 Jun 2019

Cited by 10 | Viewed by 4203

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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