Special Issue "Smart Biomedical Sensors"

A special issue of Biosensors (ISSN 2079-6374).

Deadline for manuscript submissions: closed (31 October 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Laurent A. Francis

Institute for Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain (UCL), Louvain-la-Neuve, Belgium
Website | E-Mail
Phone: +32(0)10 47 35 33
Fax: +32(0)10 47 25 98
Interests: biosensors; microfluidics; harsh environment sensing; atomic layer deposition; thin films; CMOS-MEMS; silicon-on-insulator

Special Issue Information

Dear Colleagues,

Biomedical sensors are set to revolutionise medical healthcare, in both hospitals and domestic environments. Thanks to miniaturisation efforts and the emergence of wireless communication systems, biomedical sensors are able to provide support for the continuous monitoring of various health conditions, as well as for early diagnostics and re-education. Such devices are also of paramount importance to keep an eye on the health of infants and elderly population. From non-invasive to implantable devices, the research and development of innovative biomedical sensors is a true multidisciplinary adventure, going way beyond the simple transduction of physical or biological events. Smart biomedical sensors can effectively gather and process different types of information resulting from a given situation. Such devices have also brought their benefits for impaired people, by restoring either auditive or visual perception, while the current trend is to capture and transmit more nervous signals in prosthetics. This Special Issue is dedicated to meet at the crossroads of the different research communities involved in the latest research for smart biomedical sensors and their applications, with an emphasis on emerging technologies that have a strong potential to ensure a better support to the practitioners, to restore specific body functions, or, more generally, to improve self-awareness of any medical condition.

Prof. Dr. Laurent A. Francis
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. Biosensors is an international peer-reviewed open access quarterly 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 350 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 engineering
  • Signal processing
  • Biomedical signal processing
  • Lab-on-Chip
  • Diagnosis
  • Point-of-care
  • Electrodes
  • Biocompatibility
  • Implants
  • Nerve stimulation
  • Neuronal implant
  • Cochlear implant
  • Visual implant
  • Blood
  • Tissue
  • Cells
  • CMOS integrated circuits
  • Accelerometers
  • Chemical sensors
  • Biosensors
  • Glucose sensors
  • pH sensors
  • DNA
  • Pathogens
  • Temperature
  • Breath
  • Wireless communication systems
  • Wireless sensor network
  • Imaging sensors
  • Health
  • Telemedicine
  • Oxygen sensor
  • Pressure sensor
  • Biomedical imaging
  • Surface plasmon resonance
  • Quartz microbalance
  • Electrocardiogram (ECG)
  • Electroencephalogram (EEG)

Published Papers (6 papers)

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Research

Open AccessArticle Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification
Biosensors 2018, 8(4), 101; https://doi.org/10.3390/bios8040101
Received: 28 August 2018 / Revised: 10 October 2018 / Accepted: 14 October 2018 / Published: 26 October 2018
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Abstract
Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper,
[...] Read more.
Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals. Full article
(This article belongs to the Special Issue Smart Biomedical Sensors)
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Open AccessArticle Automatic Spot Identification Method for High Throughput Surface Plasmon Resonance Imaging Analysis
Biosensors 2018, 8(3), 85; https://doi.org/10.3390/bios8030085
Received: 1 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
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Abstract
An automatic spot identification method is developed for high throughput surface plasmon resonance imaging (SPRi) analysis. As a combination of video accessing, image enhancement, image processing and parallel processing techniques, the method can identify the spots in SPRi images of the microarray from
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An automatic spot identification method is developed for high throughput surface plasmon resonance imaging (SPRi) analysis. As a combination of video accessing, image enhancement, image processing and parallel processing techniques, the method can identify the spots in SPRi images of the microarray from SPRi video data. In demonstrations of the method, SPRi video data of different protein microarrays were processed by the method. Results show that our method can locate spots in the microarray accurately regardless of the microarray pattern, spot-background contrast, light nonuniformity and spotting defects, but also can provide address information of the spots. Full article
(This article belongs to the Special Issue Smart Biomedical Sensors)
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Open AccessArticle Design and Fabrication of a BiCMOS Dielectric Sensor for Viscosity Measurements: A Possible Solution for Early Detection of COPD
Biosensors 2018, 8(3), 78; https://doi.org/10.3390/bios8030078
Received: 22 June 2018 / Revised: 8 August 2018 / Accepted: 17 August 2018 / Published: 21 August 2018
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Abstract
The viscosity variation of sputum is a common symptom of the progression of Chronic Obstructive Pulmonary Disease (COPD). Since the hydration of the sputum defines its viscosity level, dielectric sensors could be used for the characterization of sputum samples collected from patients for
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The viscosity variation of sputum is a common symptom of the progression of Chronic Obstructive Pulmonary Disease (COPD). Since the hydration of the sputum defines its viscosity level, dielectric sensors could be used for the characterization of sputum samples collected from patients for early diagnosis of COPD. In this work, a CMOS-based dielectric sensor for the real-time monitoring of sputum viscosity was designed and fabricated. A proper packaging for the ESD-protection and short-circuit prevention of the sensor was developed. The performance evaluation results show that the radio frequency sensor is capable of measuring dielectric constant of biofluids with an accuracy of 4.17%. Integration of this sensor into a portable system will result in a hand-held device capable of measuring viscosity of sputum samples of COPD-patients for diagnostic purposes. Full article
(This article belongs to the Special Issue Smart Biomedical Sensors)
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Open AccessArticle The Importance of Multifrequency Impedance Sensing of Endothelial Barrier Formation Using ECIS Technology for the Generation of a Strong and Durable Paracellular Barrier
Biosensors 2018, 8(3), 64; https://doi.org/10.3390/bios8030064
Received: 24 May 2018 / Revised: 20 June 2018 / Accepted: 28 June 2018 / Published: 4 July 2018
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Abstract
In this paper, we demonstrate the application of electrical cell-substrate impedance sensing (ECIS) technology for measuring differences in the formation of a strong and durable endothelial barrier model. In addition, we highlight the capacity of ECIS technology to model the parameters of the
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In this paper, we demonstrate the application of electrical cell-substrate impedance sensing (ECIS) technology for measuring differences in the formation of a strong and durable endothelial barrier model. In addition, we highlight the capacity of ECIS technology to model the parameters of the physical barrier associated with (I) the paracellular space (referred to as Rb) and (II) the basal adhesion of the endothelial cells (α, alpha). Physiologically, both parameters are very important for the correct formation of endothelial barriers. ECIS technology is the only commercially available technology that can measure and model these parameters independently of each other, which is important in the context of ascertaining whether a change in overall barrier resistance (R) occurs because of molecular changes in the paracellular junctional molecules or changes in the basal adhesion molecules. Finally, we show that the temporal changes observed in the paracellular Rb can be associated with changes in specific junctional proteins (CD144, ZO-1, and catenins), which have major roles in governing the overall strength of the junctional communication between neighbouring endothelial cells. Full article
(This article belongs to the Special Issue Smart Biomedical Sensors)
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Open AccessArticle Transfer Learning for Improved Audio-Based Human Activity Recognition
Biosensors 2018, 8(3), 60; https://doi.org/10.3390/bios8030060
Received: 29 May 2018 / Revised: 14 June 2018 / Accepted: 21 June 2018 / Published: 25 June 2018
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Abstract
Human activities are accompanied by characteristic sound events, the processing of which might provide valuable information for automated human activity recognition. This paper presents a novel approach addressing the case where one or more human activities are associated with limited audio data, resulting
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Human activities are accompanied by characteristic sound events, the processing of which might provide valuable information for automated human activity recognition. This paper presents a novel approach addressing the case where one or more human activities are associated with limited audio data, resulting in a potentially highly imbalanced dataset. Data augmentation is based on transfer learning; more specifically, the proposed method: (a) identifies the classes which are statistically close to the ones associated with limited data; (b) learns a multiple input, multiple output transformation; and (c) transforms the data of the closest classes so that it can be used for modeling the ones associated with limited data. Furthermore, the proposed framework includes a feature set extracted out of signal representations of diverse domains, i.e., temporal, spectral, and wavelet. Extensive experiments demonstrate the relevance of the proposed data augmentation approach under a variety of generative recognition schemes. Full article
(This article belongs to the Special Issue Smart Biomedical Sensors)
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Open AccessArticle Breathing Pattern Interpretation as an Alternative and Effective Voice Communication Solution
Biosensors 2018, 8(2), 48; https://doi.org/10.3390/bios8020048
Received: 5 March 2018 / Revised: 4 May 2018 / Accepted: 8 May 2018 / Published: 15 May 2018
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Abstract
Augmentative and alternative communication (AAC) systems tend to rely on the interpretation of purposeful gestures for interaction. Existing AAC methods could be cumbersome and limit the solutions in terms of versatility. The study aims to interpret breathing patterns (BPs) to converse with the
[...] Read more.
Augmentative and alternative communication (AAC) systems tend to rely on the interpretation of purposeful gestures for interaction. Existing AAC methods could be cumbersome and limit the solutions in terms of versatility. The study aims to interpret breathing patterns (BPs) to converse with the outside world by means of a unidirectional microphone and researches breathing-pattern interpretation (BPI) to encode messages in an interactive manner with minimal training. We present BP processing work with (1) output synthesized machine-spoken words (SMSW) along with single-channel Weiner filtering (WF) for signal de-noising, and (2) k-nearest neighbor (k-NN) classification of BPs associated with embedded dynamic time warping (DTW). An approved protocol to collect analogue modulated BP sets belonging to 4 distinct classes with 10 training BPs per class and 5 live BPs per class was implemented with 23 healthy subjects. An 86% accuracy of k-NN classification was obtained with decreasing error rates of 17%, 14%, and 11% for the live classifications of classes 2, 3, and 4, respectively. The results express a systematic reliability of 89% with increased familiarity. The outcomes from the current AAC setup recommend a durable engineering solution directly beneficial to the sufferers. Full article
(This article belongs to the Special Issue Smart Biomedical Sensors)
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