E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Biomedical Sensors and Systems 2017"

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

Deadline for manuscript submissions: 31 October 2017

Special Issue Editor

Guest Editor
Prof. Dr. Wan-Young Chung

Department of Electronic Engineering, Pukyong National University, Busan 608-737, South Korea
Website | E-Mail
Interests: biomedical sensors; biomedical signal processing; medical equipment; wireless sensor network; driver drowsiness detection; e-healthcare; BioChemLab-on-a-chip; wireless biomedical sensors

Special Issue Information

Dear Colleagues,

Recently, a wide variety of biomedical sensors such as fluid flow sensors, ultrasound sensors, chemical analysis sensors, biomaterial-based sensors, wearable biomedical sensors and wireless biomedical sensors has been used in modern medicine. Modern biomedical sensors developed with advanced microfabrication and signal processing techniques are becoming inexpensive, accurate, reliable and with excellent fit. The miniaturization of classical measurement techniques has led to the realization of complex analytical systems, including such sensors as the BioChemLab-on-a-chip. Also, with recent advances in wireless communication technology, it becomes possible to build miniature and reliable wireless biomedical sensors for e-healthcare or u-healthcare. The Special Issue will publish those full research, review and highly-rated manuscripts addressing the development of biomedical sensors and systems.

Prof. Dr. Wan-Young Chung
Guest Editor

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 monthly 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 1800 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

  • biomedical sensors
  • biomedical signal processing
  • wearable biomedical sensors
  • medical equipment
  • wireless sensor network
  • driver drowsiness detection
  • e-healthcare
  • BioChemLab-on-a-chip
  • wireless biomedical sensors

Published Papers (11 papers)

View options order results:
result details:
Displaying articles 1-11
Export citation of selected articles as:

Research

Open AccessArticle Microfluidic-Based Measurement Method of Red Blood Cell Aggregation under Hematocrit Variations
Sensors 2017, 17(9), 2037; doi:10.3390/s17092037
Received: 8 August 2017 / Revised: 2 September 2017 / Accepted: 4 September 2017 / Published: 6 September 2017
PDF Full-text (6102 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Red blood cell (RBC) aggregation and erythrocyte sedimentation rate (ESR) are considered to be promising biomarkers for effectively monitoring blood rheology at extremely low shear rates. In this study, a microfluidic-based measurement technique is suggested to evaluate RBC aggregation under hematocrit variations due
[...] Read more.
Red blood cell (RBC) aggregation and erythrocyte sedimentation rate (ESR) are considered to be promising biomarkers for effectively monitoring blood rheology at extremely low shear rates. In this study, a microfluidic-based measurement technique is suggested to evaluate RBC aggregation under hematocrit variations due to the continuous ESR. After the pipette tip is tightly fitted into an inlet port, a disposable suction pump is connected to the outlet port through a polyethylene tube. After dropping blood (approximately 0.2 mL) into the pipette tip, the blood flow can be started and stopped by periodically operating a pinch valve. To evaluate variations in RBC aggregation due to the continuous ESR, an EAI (Erythrocyte-sedimentation-rate Aggregation Index) is newly suggested, which uses temporal variations of image intensity. To demonstrate the proposed method, the dynamic characterization of the disposable suction pump is first quantitatively measured by varying the hematocrit levels and cavity volume of the suction pump. Next, variations in RBC aggregation and ESR are quantified by varying the hematocrit levels. The conventional aggregation index (AI) is maintained constant, unrelated to the hematocrit values. However, the EAI significantly decreased with respect to the hematocrit values. Thus, the EAI is more effective than the AI for monitoring variations in RBC aggregation due to the ESR. Lastly, the proposed method is employed to detect aggregated blood and thermally-induced blood. The EAI gradually increased as the concentration of a dextran solution increased. In addition, the EAI significantly decreased for thermally-induced blood. From this experimental demonstration, the proposed method is able to effectively measure variations in RBC aggregation due to continuous hematocrit variations, especially by quantifying the EAI. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution
Sensors 2017, 17(9), 1937; doi:10.3390/s17091937
Received: 21 July 2017 / Revised: 16 August 2017 / Accepted: 21 August 2017 / Published: 23 August 2017
PDF Full-text (2454 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used
[...] Read more.
This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as 88 . 8 % and 90 . 2 % , respectively, for the subject-dependent training procedure, and 80 . 8 % and 87 . 8 % , respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle The Role of Visual Noise in Influencing Mental Load and Fatigue in a Steady-State Motion Visual Evoked Potential-Based Brain-Computer Interface
Sensors 2017, 17(8), 1873; doi:10.3390/s17081873
Received: 29 June 2017 / Revised: 4 August 2017 / Accepted: 10 August 2017 / Published: 14 August 2017
PDF Full-text (4041 KB) | HTML Full-text | XML Full-text
Abstract
As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and
[...] Read more.
As a spatial selective attention-based brain-computer interface (BCI) paradigm, steady-state visual evoked potential (SSVEP) BCI has the advantages of high information transfer rate, high tolerance to artifacts, and robust performance across users. However, its benefits come at the cost of mental load and fatigue occurring in the concentration on the visual stimuli. Noise, as a ubiquitous random perturbation with the power of randomness, may be exploited by the human visual system to enhance higher-level brain functions. In this study, a novel steady-state motion visual evoked potential (SSMVEP, i.e., one kind of SSVEP)-based BCI paradigm with spatiotemporal visual noise was used to investigate the influence of noise on the compensation of mental load and fatigue deterioration during prolonged attention tasks. Changes in α, θ, θ + α powers, θ/α ratio, and electroencephalography (EEG) properties of amplitude, signal-to-noise ratio (SNR), and online accuracy, were used to evaluate mental load and fatigue. We showed that presenting a moderate visual noise to participants could reliably alleviate the mental load and fatigue during online operation of visual BCI that places demands on the attentional processes. This demonstrated that noise could provide a superior solution to the implementation of visual attention controlling-based BCI applications. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle A Novel Wearable Forehead EOG Measurement System for Human Computer Interfaces
Sensors 2017, 17(7), 1485; doi:10.3390/s17071485
Received: 2 May 2017 / Revised: 18 June 2017 / Accepted: 20 June 2017 / Published: 23 June 2017
PDF Full-text (3831 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is
[...] Read more.
Amyotrophic lateral sclerosis (ALS) patients whose voluntary muscles are paralyzed commonly communicate with the outside world using eye movement. There have been many efforts to support this method of communication by tracking or detecting eye movement. An electrooculogram (EOG), an electro-physiological signal, is generated by eye movements and can be measured with electrodes placed around the eye. In this study, we proposed a new practical electrode position on the forehead to measure EOG signals, and we developed a wearable forehead EOG measurement system for use in Human Computer/Machine interfaces (HCIs/HMIs). Four electrodes, including the ground electrode, were placed on the forehead. The two channels were arranged vertically and horizontally, sharing a positive electrode. Additionally, a real-time eye movement classification algorithm was developed based on the characteristics of the forehead EOG. Three applications were employed to evaluate the proposed system: a virtual keyboard using a modified Bremen BCI speller and an automatic sequential row-column scanner, and a drivable power wheelchair. The mean typing speeds of the modified Bremen brain–computer interface (BCI) speller and automatic row-column scanner were 10.81 and 7.74 letters per minute, and the mean classification accuracies were 91.25% and 95.12%, respectively. In the power wheelchair demonstration, the user drove the wheelchair through an 8-shape course without collision with obstacles. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle Time-Frequency Analysis of Non-Stationary Biological Signals with Sparse Linear Regression Based Fourier Linear Combiner
Sensors 2017, 17(6), 1386; doi:10.3390/s17061386
Received: 13 April 2017 / Revised: 22 May 2017 / Accepted: 22 May 2017 / Published: 14 June 2017
Cited by 1 | PDF Full-text (1162 KB) | HTML Full-text | XML Full-text
Abstract
It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a
[...] Read more.
It is often difficult to analyze biological signals because of their nonlinear and non-stationary characteristics. This necessitates the usage of time-frequency decomposition methods for analyzing the subtle changes in these signals that are often connected to an underlying phenomena. This paper presents a new approach to analyze the time-varying characteristics of such signals by employing a simple truncated Fourier series model, namely the band-limited multiple Fourier linear combiner (BMFLC). In contrast to the earlier designs, we first identified the sparsity imposed on the signal model in order to reformulate the model to a sparse linear regression model. The coefficients of the proposed model are then estimated by a convex optimization algorithm. The performance of the proposed method was analyzed with benchmark test signals. An energy ratio metric is employed to quantify the spectral performance and results show that the proposed method Sparse-BMFLC has high mean energy (0.9976) ratio and outperforms existing methods such as short-time Fourier transfrom (STFT), continuous Wavelet transform (CWT) and BMFLC Kalman Smoother. Furthermore, the proposed method provides an overall 6.22% in reconstruction error. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition
Sensors 2017, 17(6), 1370; doi:10.3390/s17061370
Received: 15 April 2017 / Revised: 5 June 2017 / Accepted: 8 June 2017 / Published: 13 June 2017
Cited by 1 | PDF Full-text (4615 KB) | HTML Full-text | XML Full-text
Abstract
Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents
[...] Read more.
Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
Sensors 2017, 17(6), 1358; doi:10.3390/s17061358
Received: 5 April 2017 / Revised: 26 May 2017 / Accepted: 7 June 2017 / Published: 11 June 2017
Cited by 2 | PDF Full-text (3662 KB) | HTML Full-text | XML Full-text
Abstract
The inductive link is widely used in implantable biomedical sensor systems to achieve near-field communication (NFC) and wireless power transfer (WPT). However, it is tough to achieve reliable NFC on an inductive WPT link when the coupling coefficient is ultra-low (0.01 typically), since
[...] Read more.
The inductive link is widely used in implantable biomedical sensor systems to achieve near-field communication (NFC) and wireless power transfer (WPT). However, it is tough to achieve reliable NFC on an inductive WPT link when the coupling coefficient is ultra-low (0.01 typically), since the NFC signal (especially for the uplink from the in-body part to the out-body part) could be too weak to be detected. Traditional load shift keying (LSK) requires strong coupling to pass the load modulation information to the power source. Instead of using LSK, we propose a dual-carrier NFC scheme for the weak-coupled inductive link; using binary phase shift keying (BPSK) modulation, its downlink data are modulated on the power carrier (2 MHz), while its uplink data are modulated on another carrier (125 kHz). The two carriers are transferred through the same coil pair. To overcome the strong interference of the power carrier, dedicated circuits are introduced. In addition, to minimize the power transfer efficiency decrease caused by adding NFC, we optimize the inductive link circuit parameters and approach the receiver sensitivity limit. In the prototype experiments, even though the coupling coefficient is as low as 0.008, the in-body transmitter costs only 0.61 mW power carrying 10 kbps of data, and achieves a 1 × 10 - 7 bit error rate under the strong interference of WPT. This dual-carrier NFC scheme could be useful for small-sized implantable biomedical sensor applications. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle Smartphone-Based pH Sensor for Home Monitoring of Pulmonary Exacerbations in Cystic Fibrosis
Sensors 2017, 17(6), 1245; doi:10.3390/s17061245
Received: 10 April 2017 / Revised: 15 May 2017 / Accepted: 23 May 2017 / Published: 30 May 2017
PDF Full-text (2969 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Currently, Cystic Fibrosis (CF) patients lack the ability to track their lung health at home, relying instead on doctor checkups leading to delayed treatment and lung damage. By leveraging the ubiquity of the smartphone to lower costs and increase portability, a smartphone-based peripheral
[...] Read more.
Currently, Cystic Fibrosis (CF) patients lack the ability to track their lung health at home, relying instead on doctor checkups leading to delayed treatment and lung damage. By leveraging the ubiquity of the smartphone to lower costs and increase portability, a smartphone-based peripheral pH measurement device was designed to attach directly to the headphone port to harvest power and communicate with a smartphone application. This platform was tested using prepared pH buffers and sputum samples from CF patients. The system matches within ~0.03 pH of a benchtop pH meter while fully powering itself and communicating with a Samsung Galaxy S3 smartphone paired with either a glass or Iridium Oxide (IrOx) electrode. The IrOx electrodes were found to have 25% higher sensitivity than the glass probes at the expense of larger drift and matrix sensitivity that can be addressed with proper calibration. The smartphone-based platform has been demonstrated as a portable replacement for laboratory pH meters, and supports both highly robust glass probes and the sensitive and miniature IrOx electrodes with calibration. This tool can enable more frequent pH sputum tracking for CF patients to help detect the onset of pulmonary exacerbation to provide timely and appropriate treatment before serious damage occurs. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms
Sensors 2017, 17(5), 1154; doi:10.3390/s17051154
Received: 24 March 2017 / Revised: 5 May 2017 / Accepted: 12 May 2017 / Published: 19 May 2017
PDF Full-text (3467 KB) | HTML Full-text | XML Full-text
Abstract
This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The
[...] Read more.
This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads
Sensors 2017, 17(5), 1135; doi:10.3390/s17051135
Received: 21 February 2017 / Revised: 6 May 2017 / Accepted: 11 May 2017 / Published: 16 May 2017
PDF Full-text (2413 KB) | HTML Full-text | XML Full-text
Abstract
The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index
[...] Read more.
The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently—the ICA-based and the QIO-based methods—which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Open AccessArticle Wearable Contactless Respiration Sensor Based on Multi-Material Fibers Integrated into Textile
Sensors 2017, 17(5), 1050; doi:10.3390/s17051050
Received: 16 March 2017 / Revised: 21 April 2017 / Accepted: 2 May 2017 / Published: 6 May 2017
PDF Full-text (3384 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we report on a novel sensor for the contactless monitoring of the respiration rate, made from multi-material fibers arranged in the form of spiral antenna (2.45 GHz central frequency). High flexibility of the used composite metal-glass-polymer fibers permits their integration
[...] Read more.
In this paper, we report on a novel sensor for the contactless monitoring of the respiration rate, made from multi-material fibers arranged in the form of spiral antenna (2.45 GHz central frequency). High flexibility of the used composite metal-glass-polymer fibers permits their integration into a cotton t-shirt without compromising comfort or restricting movement of the user. At the same time, change of the antenna geometry, due to the chest expansion and the displacement of the air volume in the lungs, is found to cause a significant shift of the antenna operational frequency, thus allowing respiration detection. In contrast with many current solutions, respiration is detected without attachment of the electrodes of any kind to the user’s body, neither direct contact of the fiber with the skin is required. Respiration patterns for two male volunteers were recorded with the help of a sensor prototype integrated into standard cotton t-shirt in sitting, standing, and lying scenarios. The typical measured frequency shift for the deep and shallow breathing was found to be in the range 120–200 MHz and 10–15 MHz, respectively. The same spiral fiber antenna is also shown to be suitable for short-range wireless communication, thus allowing respiration data transmission, for example, via the Bluetooth protocol, to mobile handheld devices. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
Figures

Figure 1

Journal Contact

MDPI AG
Sensors Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Special Issue Edit a special issue Review for Sensors
logo
loading...
Back to Top