Готові списки джерел за темами / Biomedical signal sensor

Добірка наукової літератури з теми "Biomedical signal sensor"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Biomedical signal sensor"

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Aach, T., H. Witte, and T. M. Lehmann. "Sensor, Signal and Image Informatics." Yearbook of Medical Informatics 15, no. 01 (August 2006): 57–67. http://dx.doi.org/10.1055/s-0038-1638479.

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Анотація:
SummaryThe number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments.Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CTbased diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ.The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered.Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.
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HAIDER, MOHAMMAD RAFIQUL, JEREMY HOLLEMAN, SALWA MOSTAFA, and SYED KAMRUL ISLAM. "LOW-POWER BIOMEDICAL SIGNAL MONITORING SYSTEM FOR IMPLANTABLE SENSOR APPLICATIONS." International Journal of High Speed Electronics and Systems 20, no. 01 (March 2011): 115–28. http://dx.doi.org/10.1142/s0129156411006453.

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Implantable biomedical sensors and continuous real time in vivo monitoring of various physiological parameters requires low-power sensor electronics and wireless telemetry for transmission of sensor data. In this article, generic blocks required for such systems have been demonstrated with design examples. Ideally neural or electro-chemical sensor signal monitoring units comprise of low noise amplifiers, current or voltage mode analog to digital domain data conversion circuits and wireless telemetry circuits. The low-noise amplifier described here has a novel open loop amplifier scheme used for neural signal recording systems. The design has been implemented using 0.5-μm SOI-BiCMOS process. The fabricated chip can work with 1 V supply and consumes 805 nA. The current mode analog to digital conversion signal processing circuitry takes the current signal as an input and generates a pulse-width modulated data signal. The data signal is then modulated with a high frequency carrier signal to generate FSK data for wireless transmission. The design is fabricated in 0.5-μm standard CMOS process and consumes 1.1 mW of power with 3.5 V supply.
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3

Soykan, Orhan, Michael R. Neuman, and Howard J. Chizeck. "Signal processing for sensor arrays." Annals of Biomedical Engineering 19, no. 2 (March 1991): 225–26. http://dx.doi.org/10.1007/bf02368474.

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Sosa, J., Juan A. Montiel-Nelson, R. Pulido, and Jose C. Garcia-Montesdeoca. "Design and Optimization of a Low Power Pressure Sensor for Wireless Biomedical Applications." Journal of Sensors 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/352036.

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A blood pressure sensor suitable for wireless biomedical applications is designed and optimized. State-of-the-art blood pressure sensors based on piezoresistive transducers in a full Wheatstone bridge configuration use low ohmic values because of relatively high sensitivity and low noise approach resulting in high power consumption. In this paper, the piezoresistance values are increased in order to reduce by one order of magnitude the power consumption in comparison with literature approaches. The microelectromechanical system (MEMS) pressure sensor, the mixed signal circuits signal conditioning circuitry, and the successive approximation register (SAR) analog-to-digital converter (ADC) are designed, optimized, and integrated in the same substrate using a commercial 1 μm CMOS technology. As result of the optimization, we obtained a digital sensor with high sensitivity, low noise (0.002 μV/Hz), and low power consumption (358 μW). Finally, the piezoresistance noise does not affect the pressure sensor application since its value is lower than half least significant bit (LSB) of the ADC.
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Kledrowetz, Vilem, Roman Prokop, Lukas Fujcik, Michal Pavlik, and Jiří Háze. "Low-power ASIC suitable for miniaturized wireless EMG systems." Journal of Electrical Engineering 70, no. 5 (September 1, 2019): 393–99. http://dx.doi.org/10.2478/jee-2019-0071.

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Анотація:
Abstract Nowadays, the technology advancements of signal processing, low-voltage low-power circuits and miniaturized circuits have enabled the design of compact, battery-powered, high performance solutions for a wide range of, particularly, biomedical applications. Novel sensors for human biomedical signals are creating new opportunities for low weight wearable devices which allow continuous monitoring together with freedom of movement of the users. This paper presents the design and implementation of a novel miniaturized low-power sensor in integrated circuit (IC) form suitable for wireless electromyogram (EMG) systems. Signal inputs (electrodes) are connected to this application-specific integrated circuit (ASIC). The ASIC consists of several consecutive parts. Signals from electrodes are fed to an instrumentation amplifier (INA) with fixed gain of 50 and filtered by two filters (a low-pass and high-pass filter), which remove useless signals and noise with frequencies below 20 Hz and above 500 Hz. Then signal is amplified by a variable gain amplifier. The INA together with the reconfigurable amplifier provide overall gain of 50, 200, 500 or 1250. The amplified signal is then converted to pulse density modulated (PDM) signal using a 12-bit delta-sigma modulator. The ASIC is fabricated in TSMC0.18 mixed-signal CMOS technology.
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He, Le. "Application of Biomedical Signal Acquisition Equipment in Human Sport Heart Rate Monitoring." Journal of Medical Imaging and Health Informatics 10, no. 4 (April 1, 2020): 877–83. http://dx.doi.org/10.1166/jmihi.2020.2948.

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Aiming at exploring biomedical signal acquisition equipment used in human motion heart rate monitoring, the research on the related hardware design and signal processing method was carried out. A biomedical signal acquisition device based on photoplethysmography (PPG) is designed, and the equipment was applied to acquire PPG signals and acceleration sensor signals under different motion states. The analysis of the experimental data showed that, the fusion method of the acceleration sensing information in the motion artifact removal method is perfected. The effectiveness of the baseline drift removal algorithm, motion artifact removal algorithm and dynamic heart rate monitoring algorithm was verified by reconstructing the signal quality evaluation index. To sum up, taking MINDRAY VS-800 as a reference device, it is compared with the adaptive filtering technology in terms of signal quality, BPM detection results and algorithm complexity, and better results are finally obtained.
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Li, Weiwei, Ting Jiang, and Ning Wang. "Compressed Sensing Based on the Characteristic Correlation of ECG in Hybrid Wireless Sensor Network." International Journal of Distributed Sensor Networks 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/325103.

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Анотація:
Hybrid wireless sensor network made up of wireless body area networks (WBANs) and cellular network provides support for telemedicine. In order to facilitate early diagnosis and treatment, WBANs collect and transmit crucial biomedical data to provide a continuous health monitoring by using various biomedical wireless sensors attached on or implanted in the human body. And then, collected signals are sent to a remote data center via cellular network. One of the features of WBAN is that its power consumption and sampling rate should be restricted to a minimum. Compressed sensing (CS) is an emerging signal acquisition/compression methodology which offers a prominent alternative to traditional signal acquisition. It has been proved that the successful recovery rate of multiple measurement vectors (MMV) model is higher than the single measurement vector (SMV) case. In this paper, we propose a simple algorithm of transforming the SMV model into MMV model based on the correlation of electrocardiogram (ECG), such that the MMV model can be used for general ECG signals rather than only several special signals. Experimental results show that its recovery quality is better than some existing CS-based ECG compression algorithms and sufficient for practical use.
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Sun, Ying, Rui Cao, Zhan Lu, Xin Nie, Zhaokai Li, Yonghua Yu, Hongping Tian, Xiangqun Qian, and Jianping Wang. "Design and Testing of an Impact Sensor Using Two Crossed Polyvinylidene Fluoride (PVDF) Films." Transactions of the ASABE 62, no. 5 (2019): 1195–205. http://dx.doi.org/10.13031/trans.13440.

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Abstract. Impact sensors are widely used to detect grain losses in harvesters. Using polyvinylidene fluoride (PVDF) films as sensing elements is a promising way to improve sensor performance due to their high sensitivity, stability, and flexibility. However, the overlap of collision signals significantly reduces the accuracy of a sensor. To solve this problem, a novel impact sensor with two crossed PVDF films was designed and investigated. This sensor has two orthogonal layers of sensing elements that both respond to impacts, which creates positioning information for the impacts. Because of the sensor’s structure, a signal processing method was designed based on multisensor fusion theory. Tests were performed to verify the performance of the proposed impact sensor. The average signal-to-noise ratios (SNRs) for impacted PVDF films were 34.79 and 20.23 dB, respectively, for the upper and lower layers, while the average signal-to-clutter ratios (SCRs) for nonimpacted films were 21.90 and 10.05 dB, respectively. The sensor also has an extremely high detection efficiency of at least 1528 collisions per second and can identify particles that impact at the same time. Keywords: Grain loss detection, Impact sensors, Multisensor fusion, Particle impact tests, PVDF films.
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Sezen, A. S., S. Sivaramakrishnan, S. Hur, R. Rajamani, W. Robbins, and B. J. Nelson. "Passive Wireless MEMS Microphones for Biomedical Applications." Journal of Biomechanical Engineering 127, no. 6 (July 8, 2005): 1030–34. http://dx.doi.org/10.1115/1.2049330.

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Анотація:
This paper introduces passive wireless telemetry based operation for high frequency acoustic sensors. The focus is on the development, fabrication, and evaluation of wireless, batteryless SAW-IDT MEMS microphones for biomedical applications. Due to the absence of batteries, the developed sensors are small and as a result of the batch manufacturing strategy are inexpensive which enables their utilization as disposable sensors. A pulse modulated surface acoustic wave interdigital transducer (SAW-IDT) based sensing strategy has been formulated. The sensing strategy relies on detecting the ac component of the acoustic pressure signal only and does not require calibration. The proposed sensing strategy has been successfully implemented on an in-house fabricated SAW-IDT sensor and a variable capacitor which mimics the impedance change of a capacitive microphone. Wireless telemetry distances of up to 5 centimeters have been achieved. A silicon MEMS microphone which will be used with the SAW-IDT device is being microfabricated and tested. The complete passive wireless sensor package will include the MEMS microphone wire-bonded on the SAW substrate and interrogated through an on-board antenna. This work on acoustic sensors breaks new ground by introducing high frequency (i.e., audio frequencies) sensor measurement utilizing SAW-IDT sensors. The developed sensors can be used for wireless monitoring of body sounds in a number of different applications, including monitoring breathing sounds in apnea patients, monitoring chest sounds after cardiac surgery, and for feedback sensing in compression (HFCC) vests used for respiratory ventilation. Another promising application is monitoring chest sounds in neonatal care units where the miniature sensors will minimize discomfort for the newborns.
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Anderson, William D., Sydney L. M. Wilson, and David W. Holdsworth. "Development of a Wireless Telemetry Sensor Device to Measure Load and Deformation in Orthopaedic Applications." Sensors 20, no. 23 (November 27, 2020): 6772. http://dx.doi.org/10.3390/s20236772.

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Анотація:
Due to sensor size and supporting circuitry, in-vivo load and deformation measurements are currently restricted to applications within larger orthopaedic implants. The objective of this study is to repurpose a commercially available low-power, miniature, wireless, telemetric, tire-pressure sensor (FXTH87) to measure load and deformation for future use in orthopaedic and biomedical applications. The capacitive transducer membrane was modified, and compressive deformation was applied to the transducer to determine the sensor signal value and the internal resistive force. The sensor package was embedded within a deformable enclosure to illustrate potential applications of the sensor for monitoring load. To reach the maximum output signal value, sensors required compressive deformation of 350 ± 24 µm. The output signal value of the sensor was an effective predictor of the applied load on a calibrated plastic strain member, over a range of 35 N. The FXTH87 sensor can effectively sense and transmit load-induced deformations. The sensor does not have a limit on loads it can measure, as long as deformation resulting from the applied load does not exceed 350 µm. The proposed device presents a sensitive and precise means to monitor deformation and load within small-scale, deformable enclosures.
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Більше джерел

Дисертації з теми "Biomedical signal sensor"

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Hsu, Ming-Hsuan. "MICROPROCESSOR-COMPATIBLE NEURAL SIGNAL PROCESSING FOR AN IMPLANTABLE NEURODYNAMIC SENSOR." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1244237706.

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2

Krishnan, Rajet. "Problems in distributed signal processing in wireless sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1351.

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Putra, Ramadhani Pamapta. "Implementation and Evaluation of WebAssembly Modules on Embedded System-based Basic Biomedical Sensors." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261434.

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Анотація:
WebAssembly is a new binary code specification, which was initially designed to complement JavaScript in web applications. WebAssembly is inherently portable and small, designed for multiplatform usage. Therefore, WebAssembly modules can be created to support embedded system-based biomedical sensor operation. However, WebAssembly has its own limitations to compensate with its portability. In this thesis, we show how WebAssembly modules can be applied to the basic biomedical modalities of body temperature, heart rate, and breathing pattern.  We show how the implementation performed, and what challenges were met during the development. It is concluded that WebAssembly can be applied for achieving safe and effective biomedical sensor devices, although with some limitations.
WebAssembly är ett nytt binärt maskinkodsformat, ursprungligen skapat för att komplettera JavaScript i webbapplikationer.  WebAssemblys kod är liten och kan lätt användas på flera plattformar. Därför kan WebAssembly-moduler skapas för att stödja inbyggda system för biomedicinska sensorer. WebAssembly har dock sina egna begränsningar på grund av sin portabilitet.  I denna avhandling visar vi hur WebAssembly-moduler kan användas på enkla biomedicinska mätningar av kroppstemperatur, hjärtfrekvens och andningsmönster. Vi visar hur implementeringen genomfördes och vilka utmaningar som möttes under utvecklingen. Slutsatsen är att WebAssembly kan tillämpas för att skapa säkra och effektiva biomedicinska sensorenheter, även om det finns en del begränsningar.
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Seyrafi, Aylar. "Developing Real Time Automatic Step Detection in the three dimensional Accelerometer Signal implemented on a Microcontroller System." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1183.

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Parkinson’s disease is associated with reduced coordination between respiration and locomotion. For the neurological rehabilitation research, it requires a long-time monitoring system, which enables the online analysis of the patient’s vegetative locomotor coordination. In this work a real time step detector using three-dimensional accelerometer signal for the patients with Parkinson‘s disease is developed. This step detector is a complement for a recently developed system included of intelligent, wirelessly communicating sensors. The system helps to focus on the scientific questions whether this coordination may serve as a measure for the rehabilitation progress of PD patients.
+46-762453110 +46-462886970
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Прокопчук, Артем Миколайович. "Сенсор біомедичних сигналів для цифрової електронної лабораторії". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/22972.

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Магістерська робота містить основну частину на 110 аркушах, 22 ілюстрацій, 22 таблиці кількість джерел за переліком посилань 53 джерела. Об’єктом дослідження є процес зняття електрокардіограми людини. Предметом дослідження є електроди для моніторингу біомедичних сигналів. Метою роботи є огляд роботи електродів в комплексі із датчиком ЕКГ для цифрової електронної лабораторії і запропонування оптимального варіанту електродів для подальшого застосування. Методом дослідження є теоретичний огляд існуючих різновидів біомедичних електродів та можливості їх технічного вдосконалення, а також практична перевірка роботи електродів у цифровій електронній лабораторії. Результатом роботи є отримані зображення ЕКГ при різних дослідженнях з використанням існуючих електродів та визначення оптимального варіанту електродів для застосування. Новизна результатів роботи полягає у застосуванні їх до цифрової електронної лабораторії, де будуть проводитися подальші дослідження та у визначенні вектору подальших досліджень у напрямку сухих ємнісних голчастих електродів. Результати даної роботи можуть бути використанні для подальшого їх застосування у лабораторних роботах та для проектування комбінованого типу електродів. Можливі напрямки продовження досліджень: проектування комбінованого типу сухих ємнісних голчастих електродів. Галузь застосування: навчальна цифрова електронна лабораторія, медицина.
Master's work contains the main part of 110 sheets, 22 illustrations, 22 tables and a number of sources by the list of references 53 source. The object of research is the process of taking human's electrocardiogram. The subject of the study is electrodes for monitoring biomedical signals. The aim of the work is to review the work of electrodes in conjunction with an ECG sensor for a digital electronic laboratory and to offer an optimal variant of electrodes for further application. The research method is a theoretical review of existing varieties of biomedical electrodes and the possibilities for their technical improvement, as well as practical verification of the work of electrodes in a digital electronic laboratory. The result of the work is the obtained ECG images in various studies using existing electrodes and the determination of the optimal variant of electrodes for use. The novelty of the results of the work is to apply them to a digital electronic laboratory, where further research will be carried out and in determining the vector of further research in the direction of dry capacitive needle electrodes. The results of this work can be used for their further application in laboratory work and for the design of a combined type of electrodes. Possible directions for the continuation of research: design of a combined type of dry capacitive needle electrodes. Field of application: educational Digital Electronic Laboratory, Medicine.
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6

Guo, Jing. "MULTI-MODE SELF-REFERENCING SURFACE PLASMON RESONANCE SENSORS." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/13.

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Surface-plasmon-resonance (SPR) sensors are widely used in biological, chemical, medical, and environmental sensing. This dissertation describes the design and development of dual-mode, self-referencing SPR sensors supporting two surface-plasmon modes (long- and short-range) which can differentiate surface binding interactions from bulk index changes at a single sensing location. Dual-mode SPR sensors have been optimized for surface limit of detection (LOD). In a wavelength interrogated optical setup, both surface plasmons are simultaneously excited at the same location and incident angle but at different wavelengths. To improve the sensor performance, a new approach to dual-mode SPR sensing is presented that offers improved differentiation between surface and bulk effects. By using an angular interrogation, both surface plasmons are simultaneously excited at the same location and wavelength but at different angles. Angular interrogation offers at least a factor of 3.6 improvement in surface and bulk cross-sensitivity compared to wavelength-interrogated dual-mode SPR sensors. Multi-mode SPR sensors supporting at least three surface-plasmon modes can differentiate a target surface effect from interfering surface effects and bulk index changes. This dissertation describes a tri-mode SPR sensor which supports three surface plasmon resonance modes at one single sensing position, where each mode is excited at a different wavelength. The tri-mode SPR sensor can successfully differentiate specific binding from the non-specific binding and bulk index changes.
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Rozhitskii, M. M., and O. A. Sushko. "Nanophotonic sensors for biomedical and ecological application." Thesis, B. Verkin Institute of Low Temperature Physics and Engineering, NASU, 2013. http://openarchive.nure.ua/handle/document/8873.

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Анотація:
There is an ever-increasing need to enhance the capability of sensor technology for health, structural and environmental monitoring. One area of great concern is new strains of microbial organism and the spread of infectious diseases that requires rapid identification and detection in vivo and in vitro. Another area of major concern, worldwide, is the threat of chemical and biological terrorism. This points out onto necessity of improovement of existing and development of novel detection technologies based on nanomaterials. Nanophotonics-based sensors utilizing nanostructured multiple probes provide the ability for simultaneous detection of different biomedical and ecological objects as well as the ability for remote sensing where necessary. A useful future approach can utilize nanoscale optoelectronics with hybrid detection methods involving both photonics and electronics.
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Gooch, Steven R. "A METHOD FOR NON-INVASIVE, AUTOMATED BEHAVIOR CLASSIFICATION IN MICE, USING PIEZOELECTRIC PRESSURE SENSORS." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/56.

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While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well as active behavior states, using data acquired from a Signal Solutions (Lexington, KY) piezoelectric cage floor system. The NREM/REM classification system presented an 81% agreement with human EEG scorers, indicating a useful, high throughput alternative to the widely used EEG acquisition method.
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Shublaq, Nour. "Use of inertial sensors to measure upper limb motion : application in stroke rehabilitation." Thesis, University of Oxford, 2010. http://ora.ox.ac.uk/objects/uuid:3b1709fb-8be6-4402-b846-096693fc75bc.

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Stroke is the largest cause of severe adult complex disability, caused when the blood supply to the brain is interrupted, either by a clot or a burst blood vessel. It is characterised by deficiencies in movement and balance, changes in sensation, impaired motor control and muscle tone, and bone deformity. Clinically applied stroke management relies heavily on the observational opinion of healthcare workers. Despite the proven validity of a few clinical outcome measures, they remain subjective and inconsistent, and suffer from a lack of standardisation. Motion capture of the upper limb has also been used in specialised laboratories to obtain accurate and objective information, and monitor progress in rehabilitation. However, it is unsuitable in environments that are accessible to stroke patients (for example at patients’ homes or stroke clubs), due to the high cost, special set-up and calibration requirements. The aim of this research project was to validate and assess the sensitivity of a relatively low cost, wearable, compact and easy-to-use monitoring system, which uses inertial sensors in order to obtain detailed analysis of the forearm during simple functional exercises, typically used in rehabilitation. Forearm linear and rotational motion were characterised for certain movements on four healthy subjects and a stroke patient using a motion capture system. This provided accuracy and sensitivity specifications for the wearable monitoring system. With basic signal pre-processing, the wearable system was found to report reliably on acceleration, angular velocity and orientation, with varying degrees of confidence. Integration drift errors in the estimation of linear velocity were unresolved. These errors were not straightforward to eliminate due to the varying position of the sensor accelerometer relative to gravity over time. The cyclic nature of rehabilitation exercises was exploited to improve the reliability of velocity estimation with model-based Kalman filtering, and least squares optimisation techniques. Both signal processing methods resulted in an encouraging reduction of the integration drift in velocity. Improved sensor information could provide a visual display of the movement, or determine kinematic quantities relevant to the exercise performance. Hence, the system could potentially be used to objectively inform patients and physiotherapists about progress, increasing patient motivation and improving consistency in assessment and reporting of outcomes.
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Schulz, Felipe Cubas. "Proposta de uma rede sem fio para monitoramento de sinais bioelétricos." Universidade do Estado de Santa Catarina, 2013. http://tede.udesc.br/handle/handle/1869.

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Made available in DSpace on 2016-12-12T17:38:33Z (GMT). No. of bitstreams: 1 Felipe Cubas Schulz.pdf: 4229454 bytes, checksum: cd3b6e0665b2e8aa21c05c4e5922388d (MD5) Previous issue date: 2013-08-30
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Recently, automation systems have been widely investigated. Nowadays, they are present in our lives when shopping, banking, working at home or office. Technology innovations have been increased and embedded into medical and biological equipments, where patients can be better monitored for treatment and diagnosis. These allow precise and ergonomic equipments be designed, especially when using wireless sensor networks. It is developed in this work a biomedical signal acquisition system by suing a wireless sensor network and the Zigbee technology for communication. It was implemented a system for acquiring and processing biomedical data by using commercial sensor modules for wireless communication to a host computer. Also, it was developed a graphical interface in order to manage the sensors of the network and to display the acquired signals to the user. This work has integrated there types of sensors, such as blood oxygenation, heart rate and body temperature. The sensors were chosen due to their easy accessibility and by the fact these type of signals are the most monitored in medicine. Performance tests of sensors network were made to investigate the transmission, reception and data visualization, as well as the communication distance. Also, signal acquisitions were performed in 3 healthy volunteers aged 28, 25 and 65 and the results were compared with the signals acquired by commercial equipments. The results showed that the performance of the blood oxygenation sensor was similar for the three volunteers when compared to the commercial systems. On the other hand, the measured heartbeat by the proposed system showed a greater variation. The body temperature sensor showed reliable readings with a maximum error of approximately 2%. The communication distance of the network was approximately 13 meters in an environment with walls and without the use of routers. It can be concluded that the use of Zigbee sensor network for monitoring bioelectrical signals can be easily implemented and embedded to medical equipments due to its great flexibility when compared to systems which use wired technologies.
A automação de sistemas vem se disseminando muito nos últimos anos, estando presente em nosso dia a dia quando fazemos compras, vamos ao banco ou mesmo estando em nossas casas ou trabalho. Neste contexto vem crescendo o número de oportunidades de se inserir novas tecnologias e automação também na área da medicina, onde o monitoramento de pacientes torna diagnósticos mais fáceis, precisos e ergonômicos, principalmente quando utilizamos redes de transmissão de dados sem fios. Neste trabalho foi desenvolvido um sistema de aquisição de sinais biomédicos sem fio em uma rede de sensores utilizando comunicação Zigbee. Foi implementado uma plataforma de aquisição e processamento de dados biomédicos, utilizando módulos sensores de comunicação sem fio com um computador. Também, um software foi desenvolvido para gerenciar os dispositivos presentes na rede e visualizar os sinais adquiridos ao usuário. Este trabalho integrou sensores de oxigenação do sangue, batimentos cardíacos e temperatura corporal, os quais foram escolhidos por serem considerados sinais vitais de fácil acesso. Testes de desempenho da rede de sensores foram realizados a fim de verificar a transmissão, recepção e visualização dos dados, bem como a distância de comunicação. Também, aquisição de sinais foram realizados em 3 voluntários saudáveis com idades de 28, 25 e 65 anos e os resultados foram comparados com os sinais adquiridos por equipamentos comerciais. Os resultados obtidos mostraram que o sensor de oxigenação do sangue apresentou desempenho similar para os 3 voluntários quando comparados ao sistema comercial. O sensor de batimentos cardíacos apresentou maior variação entre os valores médios pelo sistema proposto. O sensor de temperatura corporal apresentou leituras com um erro sistêmico de aproximadamente 2%. A utilização do protocolo de comunicação Zigbee em uma rede de sensores biomédicos permitiu o monitoramento contínuo de pacientes com maior flexibilidade de uso quando comparado a sistemas convencionais com tecnologias com fios. O alcance da rede chegou a aproximadamente 13 metros em um ambiente com paredes, sem o uso de roteadores. Outros sinais podem ser facilmente adicionados ao sistema e monitorados pela rede de sensores.
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Книги з теми "Biomedical signal sensor"

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Kaniusas, Eugenijus. Biomedical Signals and Sensors II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45106-9.

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Kaniusas, Eugenijus. Biomedical Signals and Sensors I. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24843-6.

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Kaniusas, Eugenijus. Biomedical Signals and Sensors III. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74917-4.

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Qingjun, Liu, and SpringerLink (Online service), eds. Biomedical Sensors and Measurement. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Danilo, De Rossi, and SpringerLink (Online service), eds. Wearable Monitoring Systems. Boston, MA: Springer Science+Business Media, LLC, 2011.

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service), SpringerLink (Online, ed. Biomedical Signals and Sensors I: Linking Physiological Phenomena and Biosignals. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Biomedical Signals And Sensors. Springer, 2012.

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Kaniusas, Eugenijus. Biomedical Signals and Sensors III: Linking Electric Biosignals and Biomedical Sensors. Springer, 2019.

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Kaniusas, Eugenijus. Biomedical Signals and Sensors II: Linking Acoustic and Optic Biosignals and Biomedical Sensors. Springer, 2015.

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Kaniusas, Eugenijus. Biomedical Signals and Sensors II: Linking Acoustic and Optic Biosignals and Biomedical Sensors. Springer, 2016.

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Частини книг з теми "Biomedical signal sensor"

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Anand, G., T. Thyagarajan, B. Aashique Roshan, L. Rajeshwar, and R. Shyam Balaji. "Signal Conditioning Circuits for GMR Sensor in Biomedical Applications." In Lecture Notes in Electrical Engineering, 93–106. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4943-1_10.

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Neumann, E. E., and A. Balbinot. "Soft Sensor for Hand-Grasping Force by Regression of an sEMG Signal." In XXVII Brazilian Congress on Biomedical Engineering, 821–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-70601-2_124.

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Islam, Syed Kamrul, Fahmida Shaheen Tulip, Kai Zhu, and Melika Roknsharifi. "Low-Power Electronics for Biomedical Sensors." In Integrated Circuits for Analog Signal Processing, 193–221. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1383-7_9.

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Kaniusas, Eugenijus. "Sensing by Acoustic Biosignals." In Biomedical Signals and Sensors II, 1–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45106-9_4.

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Kaniusas, Eugenijus. "Sensing by Optic Biosignals." In Biomedical Signals and Sensors II, 91–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45106-9_5.

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Kaniusas, Eugenijus. "Sensing by Electric Biosignals—An Introduction." In Biomedical Signals and Sensors III, 1–7. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74917-4_1.

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Kaniusas, Eugenijus. "Formation of Electric Biosignals." In Biomedical Signals and Sensors III, 9–398. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74917-4_2.

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Kaniusas, Eugenijus. "Sensing and Coupling of Electric Biosignals." In Biomedical Signals and Sensors III, 399–550. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74917-4_3.

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Kaniusas, Eugenijus. "Fundamentals of Biosignals." In Biomedical Signals and Sensors I, 1–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24843-6_1.

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Kaniusas, Eugenijus. "Physiological and Functional Basis." In Biomedical Signals and Sensors I, 27–181. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24843-6_2.

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Тези доповідей конференцій з теми "Biomedical signal sensor"

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Camilo, Tellez, Rodriguez Oscar, and Lozano Carlos. "Biomedical signal monitoring using wireless sensor networks." In 2009 IEEE Latin-American Conference on Communications (LATINCOM). IEEE, 2009. http://dx.doi.org/10.1109/latincom.2009.5305161.

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Rendek, K., M. Daricek, E. Vavrinsky, M. Donoval, and D. Donoval. "Biomedical signal amplifier for EMG wireless sensor system." In Microsystems (ASDAM). IEEE, 2010. http://dx.doi.org/10.1109/asdam.2010.5667015.

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Lee, Seungjoon, Bennett L. Ibey, Mark A. Wilson, M. N. Ericson, and Gerard L. Cote. "Wavelet signal extraction using an oximetry-based perfusion sensor." In Biomedical Optics 2004, edited by Gerard L. Cote and Alexander V. Priezzhev. SPIE, 2004. http://dx.doi.org/10.1117/12.529334.

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HAIDER, MOHAMMAD RAFIQUL, JEREMY HOLLEMAN, SALWA MOSTAFA, and SYED KAMRUL ISLAM. "LOW-POWER BIOMEDICAL SIGNAL MONITORING SYSTEM FOR IMPLANTABLE SENSOR APPLICATIONS." In Proceedings of the Workshop on Frontiers in Electronics 2009. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814383721_0010.

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Honeine, Paul, Farah Mourad, Maya Kallas, Hichem Snoussi, Hassan Amoud, and Clovis Francis. "Wireless sensor networks in biomedical: Body area networks." In 2011 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA). IEEE, 2011. http://dx.doi.org/10.1109/wosspa.2011.5931518.

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Peng, Fulai, Weidong Wang, and Hongyun Liu. "Development of a reflective PPG signal sensor." In 2014 7th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2014. http://dx.doi.org/10.1109/bmei.2014.7002847.

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Kuo, Chih-Ting, Chun-Yu Chen, Yu-Tsang Chang, Chun-Pin Lin, Chien-Ming Wu, and Chun-Ming Huang. "A nano-sensor platform utilizes tablet computer for biomedical signal processing." In 2011 IEEE First International Conference on Consumer Electronics - Berlin (ICCE-Berlin). IEEE, 2011. http://dx.doi.org/10.1109/icce-berlin.2011.6031848.

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Tobola, Andreas, Franz J. Streit, Chris Espig, Oliver Korpok, Christian Sauter, Nadine Lang, Bjorn Schmitz, et al. "Sampling rate impact on energy consumption of biomedical signal processing systems." In 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2015. http://dx.doi.org/10.1109/bsn.2015.7299392.

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Benchikh, Salam, Homa Arab, and Serioja Ovidiu Tatu. "A Novel Millimeter Wave Radar Sensor for Medical Signal Detection." In 2018 IEEE International Microwave Biomedical Conference (IMBioC). IEEE, 2018. http://dx.doi.org/10.1109/imbioc.2018.8428869.

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Meng, Qinglei, Fow-Sen Choa, Mary Kay Lobo, Hyungwoo Nam, Mohammad M. Islam, and Deepa Gupta. "Theoretical and experimental studies of transcranial alternating current stimulation (tACS) beating signal in phantoms and mice brains." In Smart Biomedical and Physiological Sensor Technology XV, edited by Brian M. Cullum, Eric S. McLamore, and Douglas Kiehl. SPIE, 2018. http://dx.doi.org/10.1117/12.2304947.

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Звіти організацій з теми "Biomedical signal sensor"

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Vingre, Anete, Peter Kolarz, and Billy Bryan. On your marks, get set, fund! Rapid responses to the Covid-19 pandemic. Fteval - Austrian Platform for Research and Technology Policy Evaluation, April 2022. http://dx.doi.org/10.22163/fteval.2022.538.

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Анотація:
This paper presents findings from an analysis of seven multidisciplinary national research funders’ responses to COVID-19. We posit that while some parts of research and innovation funding responses to COVID-19 were ‘pandemic responses’ in the conventional biomedical sense, other parts were thematically far broader and are better termed ‘societal emergency’ funding. This type of funding activity was unprecedented for many funders. Yet, it may signal a new/additional mission for research funders, which may be required to tackle future societal emergencies, medical or otherwise. Urgency (i.e., the need to deploy funding quickly) is a key distinguishing theme in these funding activities. This paper explores the different techniques that funders used to substantially speed up their application and assessment processes to ensure research on COVID-19 could commence as quickly as possible. Funders used a range of approaches, both before application submission (call design, application lengths and formats) and after (review and decision-making processes). Our research highlights a series of trade-offs, at the heart of which are concerns around simultaneously ensuring the required speed as well as the quality of funding-decisions. We extract some recommendations for what a generic ‘societal emergency’ funding toolkit might include to optimally manage these tensions in case national research funders are called upon again to respond to future crises.
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