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Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application

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

Deadline for manuscript submissions: closed (30 September 2017) | Viewed by 72993

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Prof. Dr. Yasuhisa Omura

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Department of Electrical, Electronics and Information Engineering, Kansai University, Suita 564-8680, Japan
Interests: low-energy devices for mobile sensors; quantum devices applicable to biomedical sensing; new applications of photoplethysmogram and pulse oximetry; advanced bio-impedance spectroscopy; electronic materials for sensing and their fabrication technology
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Special Issue Information

Dear Colleagues,

Many sensing devices and related circuits for biomedical informatics and other purposes are already developed and applied in the monitoring of vital data, robotics, and analyses of tissues in vivo. However, most of them assume the commercial power supply. In the 21st century, we need IoT technology that should be applied to long-term monitoring of the above-mentioned issues, such as home security, private security, including vital sensing, and so on. This demand requires low-power and low-energy devices, circuits, and well-optimized software when self-powered systems are assumed.

This Special Issue aims to highlight advances in the development, testing, and modelling of materials, devices, and circuits. The Special Issue also accepts mathematical engineering, and related proposals.

Prof. Dr. Yasuhisa Omura
Guest Editor

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Keywords

biomedical sensors
tissue monitoring in vivo
MEMS sensors
electronic materials applicable to sensors
photoplethysmogram and informatics
low-energy sensor devices
self-powered sensors and systems
bio-impedance spectroscopy
sensor-related technology

Published Papers (12 papers)

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Research

10 pages, 1932 KiB

Open AccessArticle

Online Removal of Baseline Shift with a Polynomial Function for Hemodynamic Monitoring Using Near-Infrared Spectroscopy

by Ke Zhao, Yaoyao Ji, Yan Li and Ting Li

Sensors 2018, 18(1), 312; https://doi.org/10.3390/s18010312 - 21 Jan 2018

Cited by 16 | Viewed by 5522

Abstract

Near-infrared spectroscopy (NIRS) has become widely accepted as a valuable tool for noninvasively monitoring hemodynamics for clinical and diagnostic purposes. Baseline shift has attracted great attention in the field, but there has been little quantitative study on baseline removal. Here, we aimed to study the baseline characteristics of an in-house-built portable medical NIRS device over a long time (>3.5 h). We found that the measured baselines all formed perfect polynomial functions on phantom tests mimicking human bodies, which were identified by recent NIRS studies. More importantly, our study shows that the fourth-order polynomial function acted to distinguish performance with stable and low-computation-burden fitting calibration (R-square >0.99 for all probes) among second- to sixth-order polynomials, evaluated by the parameters R-square, sum of squares due to error, and residual. This study provides a straightforward, efficient, and quantitatively evaluated solution for online baseline removal for hemodynamic monitoring using NIRS devices. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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2427 KiB

Open AccessArticle

Choice of Magnetometers and Gradiometers after Signal Space Separation

by Pilar Garcés, David López-Sanz, Fernando Maestú and Ernesto Pereda

Sensors 2017, 17(12), 2926; https://doi.org/10.3390/s17122926 - 16 Dec 2017

Cited by 58 | Viewed by 6414

Abstract

Background: Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer. Methods: First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS. Results: SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r² = 0.3–0.8 before SSS and r² > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r² > 0.8). Conclusions: After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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2541 KiB

Open AccessArticle

ESPRIT-Like Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors under the Condition of Gain and Phase Uncertainties and Mutual Coupling

by Dong Zhang, Yongshun Zhang, Guimei Zheng, Cunqian Feng and Jun Tang

Sensors 2017, 17(11), 2457; https://doi.org/10.3390/s17112457 - 26 Oct 2017

Cited by 11 | Viewed by 3781

Abstract

In this paper, we focus on the problem of two-dimensional direction of arrival (2D-DOA) estimation for monostatic MIMO Radar with electromagnetic vector received sensors (MIMO-EMVSs) under the condition of gain and phase uncertainties (GPU) and mutual coupling (MC). GPU would spoil the invariance property of the EMVSs in MIMO-EMVSs, thus the effective ESPRIT algorithm unable to be used directly. Then we put forward a C-SPD ESPRIT-like algorithm. It estimates the 2D-DOA and polarization station angle (PSA) based on the instrumental sensors method (ISM). The C-SPD ESPRIT-like algorithm can obtain good angle estimation accuracy without knowing the GPU. Furthermore, it can be applied to arbitrary array configuration and has low complexity for avoiding the angle searching procedure. When MC and GPU exist together between the elements of EMVSs, in order to make our algorithm feasible, we derive a class of separated electromagnetic vector receiver and give the S-SPD ESPRIT-like algorithm. It can solve the problem of GPU and MC efficiently. And the array configuration can be arbitrary. The effectiveness of our proposed algorithms is verified by the simulation result. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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5595 KiB

Open AccessArticle

Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine

by Dongrae Cho, Jinsil Ham, Jooyoung Oh, Jeanho Park, Sayup Kim, Nak-Kyu Lee and Boreom Lee

Sensors 2017, 17(10), 2435; https://doi.org/10.3390/s17102435 - 24 Oct 2017

Cited by 102 | Viewed by 8503

Abstract

Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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2175 KiB

Open AccessArticle

Adaptive Integration of the Compressed Algorithm of CS and NPC for the ECG Signal Compressed Algorithm in VLSI Implementation

by Yun-Hua Tseng, Yuan-Ho Chen and Chih-Wen Lu

Sensors 2017, 17(10), 2288; https://doi.org/10.3390/s17102288 - 09 Oct 2017

Cited by 15 | Viewed by 4527

Abstract

Compressed sensing (CS) is a promising approach to the compression and reconstruction of electrocardiogram (ECG) signals. It has been shown that following reconstruction, most of the changes between the original and reconstructed signals are distributed in the Q, R, and S waves (QRS) region. Furthermore, any increase in the compression ratio tends to increase the magnitude of the change. This paper presents a novel approach integrating the near-precise compressed (NPC) and CS algorithms. The simulation results presented notable improvements in signal-to-noise ratio (SNR) and compression ratio (CR). The efficacy of this approach was verified by fabricating a highly efficient low-cost chip using the Taiwan Semiconductor Manufacturing Company’s (TSMC) 0.18-μm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The proposed core has an operating frequency of 60 MHz and gate counts of 2.69 K. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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8013 KiB

Open AccessArticle

An Efficient Hardware Circuit for Spike Sorting Based on Competitive Learning Networks

by Huan-Yuan Chen, Chih-Chang Chen and Wen-Jyi Hwang

Sensors 2017, 17(10), 2232; https://doi.org/10.3390/s17102232 - 28 Sep 2017

Cited by 4 | Viewed by 5449

Abstract

This study aims to present an effective VLSI circuit for multi-channel spike sorting. The circuit supports the spike detection, feature extraction and classification operations. The detection circuit is implemented in accordance with the nonlinear energy operator algorithm. Both the peak detection and area computation operations are adopted for the realization of the hardware architecture for feature extraction. The resulting feature vectors are classified by a circuit for competitive learning (CL) neural networks. The CL circuit supports both online training and classification. In the proposed architecture, all the channels share the same detection, feature extraction, learning and classification circuits for a low area cost hardware implementation. The clock-gating technique is also employed for reducing the power dissipation. To evaluate the performance of the architecture, an application-specific integrated circuit (ASIC) implementation is presented. Experimental results demonstrate that the proposed circuit exhibits the advantages of a low chip area, a low power dissipation and a high classification success rate for spike sorting. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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9784 KiB

Open AccessArticle

A Biosensor-CMOS Platform and Integrated Readout Circuit in 0.18-μm CMOS Technology for Cancer Biomarker Detection

by Abdulaziz Alhoshany, Shilpa Sivashankar, Yousof Mashraei, Hesham Omran and Khaled N. Salama

Sensors 2017, 17(9), 1942; https://doi.org/10.3390/s17091942 - 23 Aug 2017

Cited by 19 | Viewed by 7978

Abstract

This paper presents a biosensor-CMOS platform for measuring the capacitive coupling of biorecognition elements. The biosensor is designed, fabricated, and tested for the detection and quantification of a protein that reveals the presence of early-stage cancer. For the first time, the spermidine/spermine N1 acetyltransferase (SSAT) enzyme has been screened and quantified on the surface of a capacitive sensor. The sensor surface is treated to immobilize antibodies, and the baseline capacitance of the biosensor is reduced by connecting an array of capacitors in series for fixed exposure area to the analyte. A large sensing area with small baseline capacitance is implemented to achieve a high sensitivity to SSAT enzyme concentrations. The sensed capacitance value is digitized by using a 12-bit highly digital successive-approximation capacitance-to-digital converter that is implemented in a 0.18 μm CMOS technology. The readout circuit operates in the near-subthreshold regime and provides power and area efficient operation. The capacitance range is 16.137 pF with a 4.5 fF absolute resolution, which adequately covers the concentrations of 10 mg/L, 5 mg/L, 2.5 mg/L, and 1.25 mg/L of the SSAT enzyme. The concentrations were selected as a pilot study, and the platform was shown to demonstrate high sensitivity for SSAT enzymes on the surface of the capacitive sensor. The tested prototype demonstrated 42.5 μS of measurement time and a total power consumption of 2.1 μW. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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4358 KiB

Open AccessArticle

Deep Source Localization with Magnetoencephalography Based on Sensor Array Decomposition and Beamforming

by Yegang Hu, Yicong Lin, Baoshan Yang, Guangrui Tang, Tao Liu, Yuping Wang and Jicong Zhang

Sensors 2017, 17(8), 1860; https://doi.org/10.3390/s17081860 - 11 Aug 2017

Cited by 4 | Viewed by 4539

Abstract

In recent years, the source localization technique of magnetoencephalography (MEG) has played a prominent role in cognitive neuroscience and in the diagnosis and treatment of neurological and psychological disorders. However, locating deep brain activities such as in the mesial temporal structures, especially in preoperative evaluation of epilepsy patients, may be more challenging. In this work we have proposed a modified beamforming approach for finding deep sources. First, an iterative spatiotemporal signal decomposition was employed for reconstructing the sensor arrays, which could characterize the intrinsic discriminant features for interpreting sensor signals. Next, a sensor covariance matrix was estimated under the new reconstructed space. Then, a well-known vector beamforming approach, which was a linearly constraint minimum variance (LCMV) approach, was applied to compute the solution for the inverse problem. It can be shown that the proposed source localization approach can give better localization accuracy than two other commonly-used beamforming methods (LCMV, MUSIC) in simulated MEG measurements generated with deep sources. Further, we applied the proposed approach to real MEG data recorded from ten patients with medically-refractory mesial temporal lobe epilepsy (mTLE) for finding epileptogenic zone(s), and there was a good agreement between those findings by the proposed approach and the clinical comprehensive results. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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2487 KiB

Open AccessArticle

A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry

by Chen Gong, Dake Liu, Zhidong Miao and Min Li

Sensors 2017, 17(8), 1768; https://doi.org/10.3390/s17081768 - 02 Aug 2017

Cited by 17 | Viewed by 5517

Abstract

When using the conventional two-coil inductive link for the simultaneous wireless power and data transmissions in implantable biomedical sensor devices, the strong power carrier could overwhelm the uplink data signal and even saturate the external uplink receiver. To address this problem, we propose a new magnetic-balanced inductive link for our implantable glaucoma treatment device. In this inductive link, an extra coil is specially added for the uplink receiving. The strong power carrier interference is minimized to approach zero by balanced canceling of the magnetic field of the external power coil. The implant coil is shared by the wireless power harvesting and the uplink data transmitting. Two carriers (i.e., 2-MHz power carrier and 500-kHz uplink carrier) are used for the wireless power transmission and the uplink data transmission separately. In the experiments, the prototype of this link achieves as high as 65.72 dB improvement of the signal-to-interference ratio (SIR) compared with the conventional two-coil inductive link. Benefiting from the significant improvement of SIR, the implant transmitter costs only 0.2 mW of power carrying 50 kbps of binary phase shift keying data and gets a bit error rate of 1 × 10

^{- 7}

, even though the coupling coefficient is as low as 0.005. At the same time, 5 mW is delivered to the load with maximum power transfer efficiency of 58.8%. This magnetic-balanced inductive link is useful for small-sized biomedical sensor devices, which require transmitting data and power simultaneously under ultra-weak coupling. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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5333 KiB

Open AccessArticle

A Smartphone-Based Automatic Measurement Method for Colorimetric pH Detection Using a Color Adaptation Algorithm

by Sung Deuk Kim, Youngmi Koo and Yeoheung Yun

Sensors 2017, 17(7), 1604; https://doi.org/10.3390/s17071604 - 10 Jul 2017

Cited by 60 | Viewed by 7991

Abstract

This paper proposes a smartphone-based colorimetric pH detection method using a color adaptation algorithm for point-of-care applications. Although a smartphone camera can be utilized to measure the color information of colorimetric sensors, ambient light changes and unknown built-in automatic image correction operations make it difficult to obtain stable color information. This paper utilizes a 3D printed mini light box and performs a calibration procedure with a paper-printed comparison chart and a reference image which overcomes the drawbacks of smartphone cameras and the difficulty in preparing for the calibration procedure. The color adaptation is performed in the CIE 1976 u’v’ color space by using the reference paper in order to stabilize the color variations. Non-rigid u’v’ curve interpolation is used to produce the high-resolution pH estimate. The final pH value is estimated by using the best-matching method to handle the nonlinear curve properties of multiple color patches. The experimental results obtained using a pH indicator paper show that the proposed algorithm provides reasonably good estimation of pH detection. With paper-printed accurate color comparison charts and smart color adaptation techniques, superior estimation is achieved in the smartphone-based colorimetric pH detection system for point-of-care application. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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3547 KiB

Open AccessArticle

Single-Lead Fetal ECG Extraction Based on a Parallel Marginalized Particle Filter

by Zhidong Zhao, Huiling Tong, Yanjun Deng, Wen Xu, Yefei Zhang and Haihui Ye

Sensors 2017, 17(6), 1456; https://doi.org/10.3390/s17061456 - 21 Jun 2017

Cited by 6 | Viewed by 4860

Abstract

This paper presents a novel method for extracting the fetal ECG (FECG) from a single-lead abdominal signal. A dynamical model for a modified abdominal signal is proposed, in which both the maternal ECG (MECG) and the FECG are modeled, and then a parallel marginalized particle filter (par-MPF) is used for tracking the abdominal signal. Finally, the FECG and MECG are simultaneously separated. Several experiments are conducted using both simulated and clinical signals. The results indicate that the method proposed in this paper effectively extracts the FECG and outperforms other Bayesian filtering algorithms. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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2781 KiB

Open AccessArticle

Design and Validation of a Breathing Detection System for Scuba Divers

by Corentin Altepe, S. Murat Egi, Tamer Ozyigit, D. Ruzgar Sinoplu, Alessandro Marroni and Paola Pierleoni

Sensors 2017, 17(6), 1349; https://doi.org/10.3390/s17061349 - 09 Jun 2017

Cited by 13 | Viewed by 6783

Abstract

Drowning is the major cause of death in self-contained underwater breathing apparatus (SCUBA) diving. This study proposes an embedded system with a live and light-weight algorithm which detects the breathing of divers through the analysis of the intermediate pressure (IP) signal of the SCUBA regulator. A system composed mainly of two pressure sensors and a low-power microcontroller was designed and programmed to record the pressure sensors signals and provide alarms in absence of breathing. An algorithm was developed to analyze the signals and identify inhalation events of the diver. A waterproof case was built to accommodate the system and was tested up to a depth of 25 m in a pressure chamber. To validate the system in the real environment, a series of dives with two different types of workload requiring different ranges of breathing frequencies were planned. Eight professional SCUBA divers volunteered to dive with the system to collect their IP data in order to participate to validation trials. The subjects underwent two dives, each of 52 min on average and a maximum depth of 7 m. The algorithm was optimized for the collected dataset and proved a sensitivity of inhalation detection of 97.5% and a total number of 275 false positives (FP) over a total recording time of 13.9 h. The detection algorithm presents a maximum delay of 5.2 s and requires only 800 bytes of random-access memory (RAM). The results were compared against the analysis of video records of the dives by two blinded observers and proved a sensitivity of 97.6% on the data set. The design includes a buzzer to provide audible alarms to accompanying dive buddies which will be triggered in case of degraded health conditions such as near drowning (absence of breathing), hyperventilation (breathing frequency too high) and skip-breathing (breathing frequency too low) measured by the improper breathing frequency. The system also measures the IP at rest before the dive and indicates with flashing light-emitting diodes and audible alarm the regulator malfunctions due to high or low IP that may cause fatal accidents during the dive by preventing natural breathing. It is also planned to relay the alarm signal to underwater and surface rescue authorities by means of acoustic communication. Full article

(This article belongs to the Special Issue Advanced Electronic Devices, Circuits, and Signal Processing for Biomedical Sensors Application)

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