Relevant bibliographies by topics / Bio-medical signal

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bio-medical signal'

Author: Grafiati
Published: 30 May 2022
Last updated: 11 September 2023

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Journal articles on the topic "Bio-medical signal"

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Swapna, Mudrakola, Uma Maheswari Viswanadhula, Rajanikanth Aluvalu, Vijayakumar Vardharajan, and Ketan Kotecha. "Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence." Journal of Sensor and Actuator Networks 11, no. 1 (February 25, 2022): 17. http://dx.doi.org/10.3390/jsan11010017.

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Artificial Intelligence (AI) has broadly connected the medical field at various levels of diagnosis based on the congruous data generated. Different types of bio-signal can be used to monitor a patient’s condition and in decision making. Medical equipment uses signals to communicate information to care staff. AI algorithms and approaches will help to predict health problems and check the health status of organs, while AI prediction, classification, and regression algorithms are helping the medical industry to protect from health hazards. The early prediction and detection of health conditions will guide people to stay healthy. This paper represents the scope of bio-signals using AI in the medical area. It will illustrate possible case studies relevant to bio-signals generated through IoT sensors. The bio-signals that retrospectively occur are discussed, and the new challenges of medical diagnosis using bio-signals are identified.
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Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (December 1, 2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.

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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.
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Gyuho Choi and Sungbum Pan. "Biometrics System Technology Trends Based on Bio-signal." Research Briefs on Information and Communication Technology Evolution 7 (November 15, 2021): 164–72. http://dx.doi.org/10.56801/rebicte.v7i.126.

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Biometrics is a technology that authenticates, identifies, and recognizes user using individual uniquephysical or behavioral characteristics. The scope of services is expanding with necessity and utility ina wide range of fields such as finance, security, access control, medical welfare, public service, quarantine,and entertainment. Research using bio-signal inside the body than bio-information outsidethe body is being actively conducted. In this paper, we analyze research about technologies of biometricssystems using bio-signals such as ECG, heart sound, EMG, EEG, and present the necessarytechnologies for the development direction. In the future, bio-signal based database construction incomplex conditions, deep learning network design through analyzes big data, and biometrics systemtechnologies applied in a real-time environment are expected to be studied.
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Riyadh Mahmood, Hassanein, Manaf K. Hussein, and Riyadh A. Abedraba. "Development of Low-Cost Biosignal Acquisition System for ECG, EMG, and EOG." Wasit Journal of Engineering Sciences 10, no. 3 (December 1, 2022): 191–202. http://dx.doi.org/10.31185/ejuow.vol10.iss3.352.

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The use of bio-signal is very crucial, providing enormous information concerning health and well-being of the individual. such signals can be measured and monitored by specialized devices to each bio-signal, for instance, the electrocardiogram (ECG), electromyography (EMG), electroencephalogram (EEG), and electrooculogram (EOG). Due to use of such devices, these signals could be utilized for several objectives. As it is observed in the devices of medical detection and Human to Machine Interactions (HCI). This paper presents a low-cost bio-signal collection device which is having the ability to record ECG, EMG, and EOG signals. Furthermore, STM32F103C8 system is used in Analog to Digital Conversion (ADC), with its particular application. An application has been developed in order to allow admins to observe and save the data signal simultaneously. This application has been developed by using C++ programming language and MATLAB’s code. The data signal is recorded in a format of mat file, which can be studied in details in the proposed system. This system is capitalized on Universal Serial Bus (USB) wired communication link, which is used to transmit the bio-signal through, that guarantees the safety ,avoid noise and interference. The system shows its compatiblity with various operating systems, such as, Windows, Linux, and Mac.
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Al-Zyoud, Izaldein, Fedwa Laamarti, Xiaocong Ma, Diana Tobón, and Abdulmotaleb El Saddik. "Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion." Sensors 22, no. 24 (December 12, 2022): 9747. http://dx.doi.org/10.3390/s22249747.

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Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications.
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Nimi W. S., P. Subha Hency Jose, and Jegan R. "Review on Reliable and Quality Wearable Healthcare Device (WHD)." International Journal of Reliable and Quality E-Healthcare 10, no. 4 (October 2021): 1–25. http://dx.doi.org/10.4018/ijrqeh.2021100101.

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This paper presents a brief review on present developments in wearable devices and their importance in healthcare networks. The state-of-the-art system architecture on wearable healthcare devices and their design techniques are reviewed and becomes an essential step towards developing a smart device for various biomedical applications which includes diseases classifications and detection, analyzing nature of the bio signals, vital parameters measurement, and e-health monitoring through noninvasive method. From the review on latest published research papers on medical wearable device and bio signal analysis, it can be concluded that it is more important and very essential to design and develop a smart wearable device in healthcare environment for quality signal acquisition and e-health monitoring which leads to effective measures of multiparameter extractions. This will help the medical practitioners to understand the nature of patient health condition easily by visualizing a quality signal by smart wearable devices.
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Mohanty, Mihir Narayan, and Hemanta Kumar Palo. "Machine Learning:An Effective Technique in Bio-Medical Signal Analysis and Classification." International Journal of Machine Learning and Networked Collaborative Engineering 01, no. 01 (September 30, 2017): 1–8. http://dx.doi.org/10.30991/ijmlnce.2017v01i01.001.

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Tariq, Mashal, Ayesha A. Siddiqi, Ghous Baksh Narejo, and Shehla Andleeb. "A Cross Sectional Study of Tumors Using Bio-Medical Imaging Modalities." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 1 (December 7, 2018): 66–73. http://dx.doi.org/10.2174/1573405613666170614081434.

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Background: Digital Signal Processing (D.S.P) is an evolutionary field. It has a vast variety of applications in all fields. Bio medical engineering has various applications of digital signal processing. Digital Image Processing is one of the branches of signal processing. Medical image modalities proved to be helpful for disease diagnosis. Higher expertise is required in image analysis by medical professional, either doctors or radiologists. Methods: Extensive research is being done and has produced remarkable results. The study is divided into three main parts. The first deals with introduction of mostly used imaging modalities such as, magnetic resonance imaging, x-rays, ultrasound, positron emission tomography and computed tomography. The next section includes explanation of the basic steps of digital image processing are also explained in the paper. Magnetic Resonance imaging modalities is selected for this research paper. Different methods are tested on MRI images. Discussion: Brain images are selected with and without tumor. Solid cum Cystic tumor is opted for the r esearch. Results are discussed and shown. The software used for digital image processing is MATLAB. It has in built functions which are used throughout the study. The study represents the importance of DIP for tumor segmentation and detection. Conclusion: This study provides an initial guideline for researchers from both fields, that is, medicine and engineering. The analyses are shown and discussed in detail through images. This paper shows the significance of image processing platform for tumor detection automation.
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Tunlasakun, Khanchai. "Heart Sound Monitor for Bio-Signal Learning." Advanced Materials Research 680 (April 2013): 644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.680.644.

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This research presents the design and development of the heart sound monitor for bio-signal learning which can be worked with a personal computer. The prototype will receive the heart sound via the condenser microphone built-in the stethoscope. The condenser microphone will be conversed the air pressure from heart beats to electrical signal that signal will transformed to computer via sound card. The sound card will be conversed the analog signal to digital signal for process by heart sound processing program developed by LabVIEW program. The signal will be analyzed with short time Fourier transforms in heart sound processing program by graphical user interface. The user is able to select a band pass of signal for filter and choose the power spectrum of heart sound for display. The output database from this prototype is necessary for Medical Education or Clinical Practice. The prototype was tested and it worked satisfactory.
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Mantri, Prof Shamla, Dr Pankaj Agrawal, Prof Dipti Patil, and Dr V. M. Wadhai. "Depression Analysis using ECG Signal." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 7 (November 17, 2013): 2746–51. http://dx.doi.org/10.24297/ijct.v11i7.3470.

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ECG is a bio-medical signal which records the electrical activity of the heart versus time. They are important for diagnostic and research purposes of the human heart. In this paper we discuss a method of feature extraction which is an inevitable step in most approaches in diagnosing abnormalities in the heart. A web application is developed which extracts features of ECG signal like ST segment, QRS wave, etc. and use these features for identifying whether a person suffers from any of the four levels of stress, that is, Hyper Acute stress (Myocardial Infarction), Acute stress (Type A), Hyper Chronic stress (Ischemia) or Chronic Stress (Type B). The application is built using a decision support system formed by extensive learning of behavior of the signals of various persons.Â
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Dissertations / Theses on the topic "Bio-medical signal"

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Филатова, Анна Евгеньевна. "Оценка качества структурной идентификации биомедицинских сигналов с локально сосредоточенными признаками на основе нелинейного фильтра." Thesis, Политехпериодика, 2013. http://repository.kpi.kharkov.ua/handle/KhPI-Press/46316.

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В работе предложен критерий оценки качества метода структурной идентификации биомедицинских сигналов с локально сосредоточенными признаками с помощью цифрового нелинейного фильтра. Выполнена экспериментальная проверка качества структурной идентификации при задании различных параметров нелинейного фильтра.
The quality evaluation criterion for the method of structural identification of bio-medical signals with localized features using the digital non-linear filter is proposed in this study. The quality of structural identification in the process of setting various parameters of the non-linear filter is experimentally verified.
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Belghith, Akram. "Indexation de spectres HSQC et d’images IRMf appliquée à la détection de bio-marqueurs." Thesis, Strasbourg, 2012. http://www.theses.fr/2012STRAD001/document.

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Les techniques d'acquisition des signaux médicaux sont en constante évolution et fournissent une quantité croissante de données hétérogènes qui doivent être analysées par le médecin. Dans ce contexte, des méthodes automatiques de traitement des signaux médicaux sont régulièrement proposées pour aider l'expert dans l'analyse qualitative et quantitative en facilitant leur interprétation. Ces méthodes doivent tenir compte de la physique de l'acquisition, de l'a priori que nous avons sur ces signaux et de la quantité de données à analyser pour une interprétation plus précise et plus fiable. Dans cette thèse, l'analyse des tissus biologique par spectroscopie RMN et la recherche des activités fonctionnelles cérébrales et leurs connectivités par IRMf sont explorées pour la recherche de nouveaux bio-marqueurs. Chaque information médicale sera caractérisée par un ensemble d'objets que nous cherchons à extraire, à aligner, et à coder. Le regroupement de ces objets par la mesure de leur similitude permettra leur classification et l'identification de bio-marqueurs. C'est ce schéma global d'indexation et de recherche par le contenu d'objets pour la détection des bio-marqueurs que nous proposons. Pour cela, nous nous sommes intéressés dans cette thèse à modéliser et intégrer les connaissances a priori que nous avons sur ces signaux biologiques permettant ainsi de proposer des méthodes appropriées à chaque étape d'indexation et à chaque type de signal
The medical signal acquisition techniques are constantly evolving in recent years and providing an increasing amount of data which should be then analyzed. In this context, automatic signal processing methods are regularly proposed to assist the expert in the qualitative and quantitative analysis of these images in order to facilitate their interpretation. These methods should take into account the physics of signal acquisition, the a priori we have on the signal formation and the amount of data to analyze for a more accurate and reliable interpretation. In this thesis, we focus on the two-dimensional 2D Heteronuclear Single Quantum Coherence HSQC spectra obtained by High-Resolution Magic Angle Spinning HR-MAS NMR for biological tissue analysis and the functional Magnetic Resonance Imaging fMRI images for functional brain activities analysis. Each processed medical information will be characterized by a set of objects that we seek to extract, align, and code. The clustering of these objects by measuring their similarity will allow their classification and then the identification of biomarkers. It is this global content-based object indexing and retrieval scheme that we propose. We are interested in this thesis to properly model and integrate the a priori knowledge we have on these biological signal allowing us to propose there after appropriate methods to each indexing step and each type of signal
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Momey, Fabien. "Reconstruction en tomographie dynamique par approche inverse sans compensation de mouvement." Phd thesis, Université Jean Monnet - Saint-Etienne, 2013. http://tel.archives-ouvertes.fr/tel-00842572.

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La tomographie est la discipline qui cherche à reconstruire une donnée physique dans son volume, à partir de l'information indirecte de projections intégrées de l'objet, à différents angles de vue. L'une de ses applications les plus répandues, et qui constitue le cadre de cette thèse, est l'imagerie scanner par rayons X pour le médical. Or, les mouvements inhérents à tout être vivant, typiquement le mouvement respiratoire et les battements cardiaques, posent de sérieux problèmes dans une reconstruction classique. Il est donc impératif d'en tenir compte, i.e. de reconstruire le sujet imagé comme une séquence spatio-temporelle traduisant son "évolution anatomique" au cours du temps : c'est la tomographie dynamique. Élaborer une méthode de reconstruction spécifique à ce problème est un enjeu majeur en radiothérapie, où la localisation précise de la tumeur dans le temps est un prérequis afin d'irradier les cellules cancéreuses en protégeant au mieux les tissus sains environnants. Des méthodes usuelles de reconstruction augmentent le nombre de projections acquises, permettant des reconstructions indépendantes de plusieurs phases de la séquence échantillonnée en temps. D'autres compensent directement le mouvement dans la reconstruction, en modélisant ce dernier comme un champ de déformation, estimé à partir d'un jeu de données d'acquisition antérieur. Nous proposons dans ce travail de thèse une approche nouvelle ; se basant sur la théorie des problèmes inverses, nous affranchissons la reconstruction dynamique du besoin d'accroissement de la quantité de données, ainsi que de la recherche explicite du mouvement, elle aussi consommatrice d'un surplus d'information. Nous reconstruisons la séquence dynamique à partir du seul jeu de projections courant, avec pour seules hypothèses a priori la continuité et la périodicité du mouvement. Le problème inverse est alors traité rigoureusement comme la minimisation d'un terme d'attache aux données et d'une régularisation. Nos contributions portent sur la mise au point d'une méthode de reconstruction adaptée à l'extraction optimale de l'information compte tenu de la parcimonie des données -- un aspect typique du problème dynamique -- en utilisant notamment la variation totale (TV) comme régularisation. Nous élaborons un nouveau modèle de projection tomographique précis et compétitif en temps de calcul, basé sur des fonctions B-splines séparables, permettant de repousser encore la limite de reconstruction imposée par la parcimonie. Ces développements sont ensuite insérés dans un schéma de reconstruction dynamique cohérent, appliquant notamment une régularisation TV spatio-temporelle efficace. Notre méthode exploite ainsi de façon optimale la seule information courante à disposition ; de plus sa mise en oeuvre fait preuve d'une grande simplicité. Nous faisons premièrement la démonstration de la force de notre approche sur des reconstructions 2-D+t à partir de données simulées numériquement. La faisabilité pratique de notre méthode est ensuite établie sur des reconstructions 2-D et 3-D+t à partir de données physiques "réelles", acquises sur un fantôme mécanique et sur un patient
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Muheilan, Mustafa M. "Computer engineering techniques for collecting and processing bio-medical signals associated with human eye movement." Thesis, University of Aberdeen, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.279593.

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This thesis is concerned with the investigation of two types of eye movements; smooth pursuit, and saccadic eye movement, each of which was analysed under normal condition, and then after the administration of alcohol. Parameters of interest in a selected range were measured using the novel approaches developed in this thesis and the results of a series of different tests compared. Much of the early work done in this area was based on minicomputers. Obviously, a microcomputer based system would be welcome because of costs, and the fact that they are readily available in many hospitals and health centres. The work reported here was carried out using the BBC microcomputer system, since it is inexpensive and commonly used in UK health institutes. The experimental facilities constructed for the work of this thesis were driven by the intention of producing a system free from many of the weaknesses in existing procedures, and to develop an essentially new approach to the problem. The starting point of the research described depends on the fact that whenever the eyes are moved a signal appears at the two poles of the eyes. This signal is known as the resting potential, or standing potential, with the cornea several mV positive with respect to the back of the globe. This potential is generated by the retinal pigment epithelium. By influencing the eyes to move in certain directions, and at certain velocities and frequencies, information can be gathered by further analysis of the signal captured. The signal captured is found to be very small (in the μV range), therefore an amplification of the signal is required, the amplifier needed must have specific features to meet the requirement of high input impedance so the signal is not distorted. This was achieved using a specially designed instrumentation amplifier. Noise which is always present in the signal, was rejected using filtering in analogue and digital forms. The analogue filter was a Butterworth filter with a frequency passband in the range between 0.1-30Hz. The digital filter chosen was the Hanning Window type. To ensure the safety of the person taking the tests care must be taken to isolate all equipment, consequently the signal collecting electronics was powered by batteries. The collected signal was interfaced to the computer using the 1MHz BUS of the BBC microcomputer. A second computer was used so that one of them can process the captured signal while the other generates a moving spot on the screen of a monitor as a stimulus for eye movement The collected signals are then processed in both the time and the frequency domain. The use of frequency domain techniques is a particularly useful form of analysis in the treatment of eye movement potentials, and is shown to extend the information that can be extracted from such signals.
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Pang, Wen-Yi, and 龐文頤. "Low Power Analog Signal Processor for Bio-Medical Applications." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/99398954455507336798.

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碩士
國立臺灣大學
電子工程學研究所
97
The application of VLSI technology in bio-medical instrumentation enables the emerging of the bio-MEMS and wireless technologies. By combining these technologies, personal remote sensing has become a popular research area. It applies an implantable bio-medical circuit for neural stimulation and uses RF signal to transmit recorded physiological signals. In such implanted bio-medical circuits, low power operation is very important because the heat spread caused by the implanted circuit will increase local temperature which may damage organs and neurons. This thesis presents a signal processor with area-efficient DC offset cancellation. For this processor, this work designs the building blocks of a low power 10-bit successive-approximation-register analog-to-digital converter (SAR ADC) and a low power decimation filter for bio-medical applications. In the 10-bit SAR ADC, an energy-saving capacitor array and a splitting comparator architecture is proposed to achieve low power consumption. The average switching energy of the capacitor array can be reduced by 68% compared to a conventional architecture. The splitting comparator consists of two gain paths, through which power saving for an A/D conversion is achieved by selecting the appropriate comparison path and disabling the unused path. The measured signal-to-noise-and-distortion ratio of the ADC is 58.4 dB at 500KS/s sampling rate with power consumption of 42μW from a 1-V supply. The ADC is fabricated in a 0.18-μm CMOS technology. A low-power decimation filter for portable electrocardiogram (ECG) monitoring applications is also presented. This decimation filter consists of two parts: front-end and back-end. The font-end filters noise to regain ECG signal while the back-end computes the direct current (DC) offset caused by the local oscillator (LO) leakage and subtracts it from the input. This makes the ECG signal stays within the allowable ADC input range. In addition, selecting the right decimation factors gives the most efficient design in terms of storage requirements and the number of multiplications per second (MPS). Finally, the functionality of the decimation filter is tested and verified with an Altera Stradix EP1S80 FPGA board and Tektronix TLA 715.
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Lin, Cheng-Hsiang, and 林正祥. "Electro-Optical Signal Processing Systems in Bio-medical Detection." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/62120547854015093515.

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碩士
國立中央大學
機械工程研究所
93
Owing to the tiny molecular weight and volume of biomolecules and very low physiologic concentration in biomolecular interaction analysis, it is important to improve the detection limit of biosensing. In this thesis, we integrate high sensitivity transducers (with or without fluorescence label) and developed electro-optical (E-O) signal processors to enhance the resolution of optical metrology system. First, we develop an E-O detector in bio-medical detecting application. The E-O detector combines with a photomultiplier tube sensor and a developed circuit board including the analog current amplifier, analog to digital converter, and universal serial bus (USB) interface. The detector now can measure the light power down to 10-16W and has been used in the bio-luminescence system and biochip fluorescent scanning reader. Moreover, a microfluid biochip is used to verify and the signal-to-noise ratio of the fluorescent signal is improved with the amplitude modulation lock-in amplifying technique with the help of dual-phase lock-in amplifier, and therefore the detection limit of the fluorescence measurement is improved with 20 times better then that of a conventional system. Lock-in amplifier is a key E-O device, so, we develop a home-made digital lock-in amplifier based on a home-made 32-bit digital signal processing board with USB 2.0 interface to realize the digital lock-in amplifier technique in real-time data transmission. To develop label-free biosensing systems, we focus on high sensitivity surface plasmon resonance (SPR) biosensing to build a common-path SPR heterodyne interferometer with the above E-O devices. The SPR interferometer can detect the refractive index change of better than 10-6 by testing the nitrogen and argon gases. Besides, we compare the difference between the magneto-optical and E-O modulation light sources. Finally, a prototype of full-field heterodyne interferometer is developed.
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Shieh, Hsiu-Li, and 謝秀利. "Study on Bio-medical Array Sensors and Signal Readout Circuits." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/azy6q9.

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Pang, Tz-Han, and 龎子涵. "A Low-Complexity Bio-medical Signal Receiver for Wireless Body Area Network." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/94365758275948280003.

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碩士
國立中興大學
電機工程學系所
99
In recent years, the wireless communication technology has been developed with a very high speed. In accordance to the tendency towards an aging society, the wireless communications technology has been used in medical monitoring gradually, such as home health monitoring, telemedicine, bio-sensing, smart device near body and so on. Such devices are all with characteristics of low power consumption, low cost, and low complexity. Thus, we want to construct a smart bio-sensing system, which is wireless, tiny, and can be provided for more than one person to use at the same time. The bio-signal between users will not be interfered with each other. The sensing bio-signal will be sent to the smart analyzing system by wireless transmission. Once the unusual signal is detected, the smart analyzing system will send out a warning signal. The system can save a lot of medical officers and resources. This thesis accomplished the baseband receiver for wireless bio-medical signal transmission.Like the other wireless transmission standard, this thesis also considered the channel effect like AWGN, carrier frequency offset, and phase noise. To reduce the complexity of the baseband receiver, many algorisms have been carefully investigated, such as packet detector, the compensation and estimation for carrier frequency offset, energy detector, boundary synchronism, and dispreading. After the algorisms for various functions are determined, then it is verified and accomplished by Verilog and FPGA.
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Yen, Chih-Jen, and 顏志仁. "Analog Integrated Circuit Design for Bio-Signal Measurement and Medical System Application." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/58909975997208127511.

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碩士
中原大學
電機工程學系
87
The objective of this dissertation is to design and implement the analog integrated circuit chips for the wireless bio-signal transmission system. By the integrating method, it can achieve minimizing the occupied area, consuming little power, making the cost down and using conveniently. The analog integrated circuit chips have been used in the medical system application to process the physiological signal. The source of the signal is most coming from the electrocardiograpy (ECG). All these designed analog integrated circuits are based on a generic CMOS two-stage operational amplifier (op-amp). Design and characteristics of the CMOS two-stage op-amp has been presented in this dissertation. By using the op-amp, other analog circuits could be constructed, such as instrumentation amplifier, gain amplifier, switched-capacitor lowpass filter, and A/D converter. They are all integrated into chips. Before the fabrication of chips, these building blocks had simulated by HSPICE. The simulation results must meet the specifications. Then draw the circuit layout and simulate again (such as verification of DRC, LVS and LPE) until all the performance meet the specifications. The fabrication of chips uses the UMC 0.5μm double-poly double-metal CMOS technology. In order to identify the performance of these chips, The experimental on-board system constructed by using discrete commercial chips and designed chips have been verified in this research. The results showed that it meets the system specification. It is proved that by the integrating method, the occupied area can be minimized, and the expense of the system can also be reduced. Also, it is convenient to use.
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Wang, Wei-Sheng, and 王韋盛. "A 1.6μW Successive Approximation analog-to-digital Converter for Bio-medical Signal Application." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/52285246553940087883.

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Abstract:
碩士
國立清華大學
電機工程學系
100
This thesis proposes a novel 0.9V 10-bit Successive Approximation (SAR) analog-to-digital converter (ADC) based on half junction splitting (J.S.) and half binary weighted capacitor digital-to-analog converter (DAC) architecture. The kick-back noise of this structure due to comparator is larger than other DAC structures, thus a modified rail-to-rail comparator is used to reduce kick-back noise. This ADC is implemented in sub-threshold to reduce power consumption. In addition, dummy comparators are used in different sections of DAC to reduce the offset voltage caused by different gain errors of different DAC sections. The pre-simulation shows that the power dissipation is 1.27μW, SNDR is 61.7dB, ENOB is 9.96-bit, and figure-of-merit (FOM) is 12.8 fJ/conversion step. The chip has been fabricated with TMSC 0.18μm 1P6M CMOS process. The chip area is 893�e893μm2 with pads, and the core area is 440�e430μm2. The post-layout simulation shows that the power consumption is 1.72μW, the SNDR is 59.1dB, ENOB is 9.53-bit, and FOM is 23.2 fJ/conversion step. Under 0.9V supply voltage and 100KS/s sampling rate, the measurement result shows that the power dissipation was 1.59μW, SNDR was 46.47dB, ENOB was 7.43-bit, and FOM was 92.2 fJ/conversion step. This chip worked under 0.6 V supply voltage and consumed only 0.783μW. This low-power ADC is suitable for bio-medical signal acquisition. This low-power ADC is suitable for bio-medical signal acquisition.
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Books on the topic "Bio-medical signal"

1

Data mining in biomedical imaging, signaling, and systems. Boca Raton: CRC Press, 2011.

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Rundo, Francesco, Giuseppe Luigi Banna, Concetto Spampinato, and Sabrina Conoci, eds. Bio-inspired Physiological Signal(s) and Medical Image(s) Neural Processing Systems Based on Deep Learning and Mathematical Modeling for Implementing Bio-Engineering Applications in Medical and Industrial Fields. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-916-7.

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Book chapters on the topic "Bio-medical signal"

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Havlík, Jan, Ondřej Fousek, and Miroslav Ložek. "Patient Monitoring Using Bioimpedance Signal." In Information Technology in Bio- and Medical Informatics, 171–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32395-9_15.

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Havlík, Jan, Miroslav Ložek, Matouš Pokorný, Jakub Parák, Petr Huňka, and Lenka Lhotská. "Adaptive Model of Cardiovascular System: Realization and Signal Database." In Information Technology in Bio- and Medical Informatics, 112–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40093-3_11.

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Thenmozhi, S., Ramgopal Segu, Shahla Sohail, and P. Sureka. "Implementation of Point of Care System Using Bio-medical Signal Steganography." In New Trends in Computational Vision and Bio-inspired Computing, 89–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41862-5_9.

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Shahbazi, Amir, Nasrin Afsarimanesh, Tele Tan, Ghobad Shafiei Sabet, and Gabriel Yin Foo Lee. "Fundamentals of Bio-Signal Sensor Design and Development in Medical Applications." In Sensing Technology, 431–39. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29871-4_44.

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Sachin Saj, T. K., V. Sowmya, and K. P. Soman. "Performance Analysis of Segmentor Adversarial Network (SegAN) on Bio-Medical Images for Image Segmentation." In Advances in Automation, Signal Processing, Instrumentation, and Control, 751–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_69.

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Valentová, Helena, and Jan Havlík. "Initial Analysis of the EEG Signal Processing Methods for Studying Correlations between Muscle and Brain Activity." In Information Technology in Bio- and Medical Informatics, ITBAM 2010, 220–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15020-3_20.

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Karthick, M., C. Jeyalakshmi, and B. Murugeshwari. "Comparative Analysis of EMG Bio Signal Based on Empirical Wavelet Transform for Medical Diagnosis." In New Trends in Computational Vision and Bio-inspired Computing, 1087–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41862-5_110.

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Havlík, Jan, Lucie Kučerová, Imrich Kohút, Jan Dvořák, and Vratislav Fabián. "The Database of the Cardiovascular System Related Signals." In Information Technology in Bio- and Medical Informatics, 169–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32395-9_14.

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Li, Jiefu, Jung-Youn Lee, and Li Liao. "Detecting De Novo Plasmodesmata Targeting Signals and Identifying PD Targeting Proteins." In Computational Advances in Bio and Medical Sciences, 1–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46165-2_1.

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Mohanty, Mihir Narayan, and Aurobinda Routray. "Estimation of Autocorrelation Space for Classification of Bio-medical Signals." In Swarm, Evolutionary, and Memetic Computing, 697–704. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35380-2_81.

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Conference papers on the topic "Bio-medical signal"

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Mandavi, Prasannjit, Nilotpal Mrinal, Kalyan Chatterjee, and Dasgupta S. "Data Compression Using Neural Networks in Bio-Medical Signal Processing." In National Conference on Advancement of Computing in Engineering Research. Academy & Industry Research Collaboration Center (AIRCC), 2013. http://dx.doi.org/10.5121/csit.2013.3215.

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Raman, Suraj Kiran, Jayadev Kumar Jayaram, Sidhaarth Murugan, Arnab Saha, and R. K. Kavitha. "Design of a robust method to acquire EOG signals using Bio-medical signal processing." In 2016 Online International Conference on Green Engineering and Technologies (IC-GET). IEEE, 2016. http://dx.doi.org/10.1109/get.2016.7916631.

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Deeksha, B., A. Sai Ravi Teja, E. Sai Laxshmi, M. Nikhil Eshwar, Ashish Singh, and Mohammad Aneesh. "Electromagnetically coupled notches loaded patch antenna for bio-medical applications." In 2017 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). IEEE, 2017. http://dx.doi.org/10.1109/mspct.2017.8364022.

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Chih-Hung Lin, Robert Chen-Hao Chang, Tz-Han Pang, and Kuang-Hao Lin. "A low-complexity bio-medical signal receiver for wireless body area network." In 2012 International SoC Design Conference (ISOCC 2012). IEEE, 2012. http://dx.doi.org/10.1109/isocc.2012.6406891.

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Spence, G. "Blind signal separation and its application to long-term bio-medical monitoring." In 3rd IEE International Seminar on Medical Applications of Signal Processing. IEE, 2005. http://dx.doi.org/10.1049/ic:20050338.

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Sankar, Aishwarya, and Rajeswari Sridhar. "Gene interactions and influences identification for diabetes from bio-medical literature." In 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2017. http://dx.doi.org/10.1109/iccsp.2017.8286424.

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Zhao, Xiaolan, Zuguo Wu, Jiren Xu, Keren Wang, and Jihai Niu. "Speech Signal Feature Extraction Based on Wavelet Transform." In 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI). IEEE, 2011. http://dx.doi.org/10.1109/icbmi.2011.80.

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Hua, Shaoyan, Ming Yuchi, and Mingyue Ding. "Compressed Sensing for RF Signal Reconstruction in B-model Ultrasound Imaging." In 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI). IEEE, 2011. http://dx.doi.org/10.1109/icbmi.2011.8.

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Mal, Ashis Kumar, and Rishi Todani. "Non Overlapping Clock generator for switched capacitor circuits in Bio-Medical applications." In 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN). IEEE, 2011. http://dx.doi.org/10.1109/icsccn.2011.6024551.

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Shenoy, Meetha, Kirti Varghese, and Manasa Upadhyaya. "A 8-bit SAR ADC using current mode approach for bio-medical applications." In 2014 National Conference on Communication, Signal Processing and Networking (NCCSN). IEEE, 2014. http://dx.doi.org/10.1109/nccsn.2014.7001152.

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