Thematische Bibliographien / ECG extraction

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte

Auswahl der wissenschaftlichen Literatur zum Thema „ECG extraction“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 14. Februar 2022

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Zeitschriftenartikel zum Thema "ECG extraction"

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Gohil, Heena Jaysukh. „Real Time ECG Extraction“. International Journal for Research in Applied Science and Engineering Technology 8, Nr. 2 (29.02.2020): 716–21. http://dx.doi.org/10.22214/ijraset.2020.2110.

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R, Rasu, P. Shanmugasundaram und N. Santhiyakumari. „Fetal ECG Extraction from Maternal ECG using MATLAB“. i-manager's Journal on Digital Signal Processing 3, Nr. 1 (15.03.2015): 7–11. http://dx.doi.org/10.26634/jdp.3.1.3284.

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Chandra, Shanti, Ambalika Sharma und Girish Kumar Singh. „Feature extraction of ECG signal“. Journal of Medical Engineering & Technology 42, Nr. 4 (19.05.2018): 306–16. http://dx.doi.org/10.1080/03091902.2018.1492039.

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Choi, Chul-Hyung, Young-Pil Kim, Si-Kyung Kim, Jeong-Bong You und Bong-Gyun Seo. „Mobile ECG Measurement System Design with Fetal ECG Extraction Capability“. Transactions of The Korean Institute of Electrical Engineers 66, Nr. 2 (01.02.2017): 431–38. http://dx.doi.org/10.5370/kiee.2017.66.2.431.

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HASAN, M. A., M. I. IBRAHIMY und M. B. I. REAZ. „Fetal ECG Extraction from Maternal Abdominal ECG Using Neural Network“. Journal of Software Engineering and Applications 02, Nr. 05 (2009): 330–34. http://dx.doi.org/10.4236/jsea.2009.25043.

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Selva Viji, C. Kezi, M. E. ,. P. Kanagasabap . und Stanley Johnson . „Fetal ECG Extraction using Softcomputing Technique“. Journal of Applied Sciences 6, Nr. 2 (01.01.2006): 251–56. http://dx.doi.org/10.3923/jas.2006.251.256.

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Bhyri, Channappa, S. T. Hamde und L. M. Waghmare. „ECG feature extraction and disease diagnosis“. Journal of Medical Engineering & Technology 35, Nr. 6-7 (20.07.2011): 354–61. http://dx.doi.org/10.3109/03091902.2011.595530.

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Raj, Chinmayee G., V. Sri Harsha, B. Sai Gowthami und Sunitha R. „Virtual Instrumentation Based Fetal ECG Extraction“. Procedia Computer Science 70 (2015): 289–95. http://dx.doi.org/10.1016/j.procs.2015.10.093.

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9

Jen, K. K., und Y. R. Hwang. „Long-term ECG signal feature extraction“. Journal of Medical Engineering & Technology 31, Nr. 3 (Januar 2007): 202–9. http://dx.doi.org/10.1080/03091900600718675.

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S V, Vinoth, und Kumarganesh S. „Fetal ECG Extraction using LMS Filter“. International Journal of Electronics and Communication Engineering 3, Nr. 11 (25.11.2016): 3–5. http://dx.doi.org/10.14445/23488549/ijece-v3i11p111.

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Dissertationen zum Thema "ECG extraction"

1

Peddaneni, Hemanth. „Comparison of algorithms for fetal ECG extraction“. [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0007480.

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Niknazar, Mohammad. „Extraction et débruitage de signaux ECG du foetus“. Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00954175.

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Les malformations cardiaques congénitales sont la première cause de décès liés à une anomalie congénitale. L''electrocardiogramme du fœtus (ECGf), qui est censé contenir beaucoup plus d'informations par rapport aux méthodes échographiques conventionnelles, peut ˆêtre mesuré'e par des électrodes sur l'abdomen de la mère. Cependant, il est tr'es faible et mélangé avec plusieurs sources de bruit et interférence y compris l'ECG de la mère (ECGm) dont le niveau est très fort. Dans les études précédentes, plusieurs méthodes ont été proposées pour l'extraction de l'ECGf à partir des signaux enregistrés par des électrodes placées à la surface du corps de la mère. Cependant, ces méthodes nécessitent un nombre de capteurs important, et s'avèrent inefficaces avec un ou deux capteurs. Dans cette étude trois approches innovantes reposant sur une paramétrisation algébrique, statistique ou par variables d'état sont proposées. Ces trois méthodes mettent en œuvre des modélisations différentes de la quasi-périodicité du signal cardiaque. Dans la première approche, le signal cardiaque et sa variabilité sont modélisés par un filtre de Kalman. Dans la seconde approche, le signal est découpé en fenêtres selon les battements, et l'empilage constitue un tenseur dont on cherchera la décomposition. Dans la troisième approche, le signal n'est pas modélisé directement, mais il est considéré comme un processus Gaussien, caractérisé par ses statistiques à l'ordre deux. Dans les différentes modèles, contrairement aux études précédentes, l'ECGm et le (ou les) ECGf sont modélisés explicitement. Les performances des méthodes proposées, qui utilisent un nombre minimum de capteurs, sont évaluées sur des données synthétiques et des enregistrements réels, y compris les signaux cardiaques des fœtus jumeaux.
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Michael, Pratheek. „Simulation Studies on ECG Vector Dipole Extraction in Liquid Medium“. Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6625.

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To circumvent some inherent problems in the conventional ECG, this research reinvestigates an ‘unassisted’ approach which enables ECG measurement without the placement of leads on the body. Employed in this research is a widely accepted assumption that the electrical activity of the heart may be represented, largely, by a 3-D time-varying current dipole (3D-CD). From the PhysioBank database, mECG and fECG data were obtained, and Singular Value Decomposition (SVD) was performed to estimate the time-varying Vector ECG dipole. To determine the sensing matrix responsible for transforming the activity of the 3D-CD into the potential distribution on the surface of the medium, the ECG vector dipole signals are used to excite a 3D-CD in water medium of a specific shape-containing-ellipsoid model(s) in COMSOL tool. The sensing matrix thereby estimated is then utilized to reconstruct the 3D-CD signals from the signals measured by the probes on the surface of the medium. Fairly low NRMSEs (Normalized Root-Mean-Squared Errors) are attained. The approach is also successfully extended to the case of two ellipsoids, one inside the other, representing a pregnant female subject. Low NRMSEs (Normalized Root-Mean-Squared Errors) are again observed.
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Darrington, John Mark. „Real time extraction of ECG fiducial points using shape based detection“. University of Western Australia. School of Computer Science and Software Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0152.

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The electrocardiograph (ECG) is a common clinical and biomedical research tool used for both diagnostic and prognostic purposes. In recent years computer aided analysis of the ECG has enabled cardiographic patterns to be found which were hitherto not apparent. Many of these analyses rely upon the segmentation of the ECG into separate time delimited waveforms. The instants delimiting these segments are called the
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Bin, Safie Sairul Izwan. „Pulse domain novel feature extraction methods with application to ecg biometric authentication“. Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=17829.

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This thesis presents the concept of representing finite signals in terms of sequential output pulses called pulse domain to extract Electrocardiogram (ECG) features for biometric authentication systems. Two novel methods based on the pulse domain philosophy namely Pulse Active (PA) and Adaptive Pulse Active (APA) techniques are presented in this thesis. A total of 11 algorithms are derived from these two methods and used to generate novel ECG feature vectors. Six algorithms of the PA technique are named as Pulse Active Bit (PAB), Pulse Active Width (PAW), Pulse Active Area (PAA), Pulse Active Mean (PAM), Pulse Active Ratio (PAR) and Pulse Active Harmonic (PAH). Five APA algorithms are named as Adaptive Pulse Active Bit (APAB), Adaptive Pulse Active Width (APAW), Adaptive Pulse Active Area (APAA), Adaptive Pulse Active Mean (APAM) and Adaptive Pulse Active Harmonic (APAH). The proposed techniques are validated using ECG experimental data from 112 subjects. Simulation results indicate that APAW generates the best biometric performance of all 11 algorithms. Selected ranges of PA and APA parameters are determined in this thesis that generates approximate similar biometric performance. Using this suggested range, these parameters are than used as a personal identification number (PIN) which are a part of the proposed PA-APA ECG based multilevel security biometric authentication system.
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Janjarasjitt, Suparerk. „A NEW QRS DETECTION AND ECG SIGNAL EXTRACTION TECHNIQUE FOR FETAL MONITORING“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1144263231.

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Tang, Yu. „Feature Extraction for the Cardiovascular Disease Diagnosis“. Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33742.

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Cardiovascular disease is a serious life-threatening disease. It can occur suddenly and progresses rapidly. Finding the right disease features in the early stage is important to decrease the number of deaths and to make sure that the patient can fully recover. Though there are several methods of examination, describing heart activities in signal form is the most cost-effective way. In this case, ECG is the best choice because it can record heart activity in signal form and it is safer, faster and more convenient than other methods of examination. However, there are still problems involved in the ECG. For example, not all the ECG features are clear and easily understood. In addition, the frequency features are not present in the traditional ECG. To solve these problems, the project uses the optimized CWT algorithm to transform data from the time domain into the time-frequency domain. The result is evaluated by three data mining algorithms with different mechanisms. The evaluation proves that the features in the ECG are successfully extracted and important diagnostic information in the ECG is preserved. A user interface is designed increasing efficiency, which facilitates the implementation.
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Islam, Mohd Siblee. „A Decision Support System for StressDiagnosis using ECG Sensor“. Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-11769.

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Diagnosis of stress is important because it can cause many diseases e.g., heart disease, headache, migraine, sleep problems, irritability etc. Diagnosis of stress in patients often involves acquisition of biological signals for example heart rate, finger temperature, electrocardiogram (ECG), electromyography signal (EMG), skin conductance signal (SC) etc. followed up by a careful analysis of the acquired signals. The accuracy is totally dependent on the experience of an expert. Again the number of such experts is also very limited. Heart rate is considered as an important parameter in determining stress. It reflects status of the autonomic nervous system (ANS) and thus is very effective in monitoring any imbalance in patient’s stress level. Therefore, a computer-aided system is useful to determine stress level based on various features that can be extracted from a patient’s heart rate signals. Stress diagnosis using biomedical signals is difficult and since the biomedical signals are too complex to generate any rule an experienced person or expert is needed to determine stress levels. Also, it is not feasible to use all the features that are available or possible to extract from the signal. So, relevant features should be chosen from the extracted features that are capable to diagnose stress. Again, ECG signal is frequently contaminated by outliers produced by the loose conduction of the electrode due to sneezing, itching etcetera that hampers the value of the features. A Case-Based Reasoning (CBR) System is helpful when it is really hard to formulate rule and the knowledge on the domain is also weak. A CBR system is developed to evaluate how closely it can diagnose stress levels compare to an expert. A study is done to find out mostly used features to reduce the number of features used in the system and in case library. A software prototype is developed that can collect ECG signal from a patient through ECG sensor and calculate Inter Beat Interval (IBI) signal and features from it. Instead of doing manual visual inspection a new way to remove outliers from the IBI signal is also proposed and implemented here. The case base has been initiated with 22 reference cases classified by an expert. A performance analysis has been done and the result considering how close the system can perform compare to the expert is presented. On the basis of the evaluations an accuracy of 86% is obtained compare to an expert. However, the correctly classified case for stressed group (Sensitivity) was 57% and it is quite important to increase as it is related to the safety issue of health. The reasons of relatively lower sensitivity and possible ways to improve it are also investigated and explained.
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Koc, Bengi. „Detection And Classification Of Qrs Complexes From The Ecg Recordings“. Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12610328/index.pdf.

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Electrocardiography (ECG) is the most important noninvasive tool used for diagnosing heart diseases. An ECG interpretation program can help the physician state the diagnosis correctly and take the corrective action. Detection of the QRS complexes from the ECG signal is usually the first step for an interpretation tool. The main goal in this thesis was to develop robust and high performance QRS detection algorithms, and using the results of the QRS detection step, to classify these beats according to their different pathologies. In order to evaluate the performances, these algorithms were tested and compared in Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database, which was developed for research in cardiac electrophysiology. In this thesis, four promising QRS detection methods were taken from literature and implemented: a derivative based method (Method I), a digital filter based method (Method II), Tompkin&rsquo
s method that utilizes the morphological features of the ECG signal (Method III) and a neural network based QRS detection method (Method IV). Overall sensitivity and positive predictivity values above 99% are achieved with each method, which are compatible with the results reported in literature. Method III has the best overall performance among the others with a sensitivity of 99.93% and a positive predictivity of 100.00%. Based on the detected QRS complexes, some features were extracted and classification of some beat types were performed. In order to classify the detected beats, three methods were taken from literature and implemented in this thesis: a Kth nearest neighbor rule based method (Method I), a neural network based method (Method II) and a rule based method (Method III). Overall results of Method I and Method II have sensitivity values above 92.96%. These findings are also compatible with those reported in the related literature. The classification made by the rule based approach, Method III, did not coincide well with the annotations provided in the MIT-BIH database. The best results were achieved by Method II with the overall sensitivity value of 95.24%.
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Noorzadeh, Saman. „Extraction de l'ECG du foetus et de ses caractéristiques grâce à la multi-modalité“. Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT135/document.

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La surveillance de la santé foetale permet aux cliniciens d’évaluer le bien-être du foetus,de faire une détection précoce des anomalies cardiaques foetales et de fournir les traitementsappropriés. Les développements technologies actuels visent à permettre la mesurede l’électrocardiogramme (ECG) foetal de façon non-invasive afin d’extraire non seulementle rythme cardiaque mais également la forme d’onde du signal. Cet objectif est rendudifficile par le faible rapport signal sur bruit des signaux mesurés sur l’abdomen maternel.Cette mesure est donc toujours un challenge auquel se confrontent beaucoup d’études quiproposent des solutions de traitement de signal basées sur la seule modalité ECG.Le but de cette thèse est d’utiliser la modélisation des processus Gaussiens pour améliorerl’extraction des signaux cardiaques foetaux, dans une base multi-modale. L’ECG est utiliséconjointement avec le signal Phonocardiogramme (PCG) qui peut apporter une informationcomplémentaire à l’ECG. Une méthode générale pour la modélisation des signauxquasi-périodiques est présentée avec l’application au débruitage de l’ECG et à l’extractionde l’ECG du foetus. Différents aspects de la multi-modalité (synchronisation, · · · ) proposéesont étudiées afin de détecter avec plus de robustesse les battements cardiaques foetaux.La méthode considère l’application sur les signaux ECG et PCG à travers deux aspects:l’aspect du traitement du signal et l’expérimental. La modélisation des processus Gaussien,avec le signal PCG pris comme la référence, est utilisée pour extraire des modèles flexibleset des estimations non linéaires de l’information. La méthode cherche également à faciliterla mise en oeuvre pratique en utilisant un codage 1-bit des signaux de référence.Le modèle proposé est validé sur des signaux synthétiques et également sur des donnéespréliminaires réelles qui ont été enregistrées afin d’amorcer la constitution d’une base dedonnées multi-modale synchronisée. Les premiers résultats montrent que la méthode permettraà terme aux cliniciens d’étudier les battements cardiaques ainsi que la morphologiede l’ECG. Ce dernier aspect était jusqu’à présent limité à l’analyse d’enregistrements ECGinvasifs prélevés pendant l’accouchement par le biais d’électrodes posées sur le scalp dufoetus
Fetal health must be carefully monitored during pregnancy to detect early fetal cardiac diseases, and provide appropriate treatment. Technological development allows a monitoring during pregnancy using the non-invasive fetal electrocardiogram (ECG). Noninvasive fetal ECG is a method not only to detect fetal heart rate, but also to analyze the morphology of fetal ECG, which is now limited to analysis of the invasive ECG during delivery. However, the noninvasive fetal ECG recorded from the mother's abdomen is contaminated with several noise sources among which the maternal ECG is the most prominent.In the present study, the problem of noninvasive fetal ECG extraction is tackled using multi-modality. Beside ECG signal, this approach benefits from the Phonocardiogram (PCG) signal as another signal modality, which can provide complementary information about the fetal ECG.A general method for quasi-periodic signal analysis and modeling is first described and its application to ECG denoising and fetal ECG extraction is explained. Considering the difficulties caused by the synchronization of the two modalities, the event detection in the quasi-periodic signals is also studied which can be specified to the detection of the R-peaks in the ECG signal.The method considers both clinical and signal processing aspects of the application on ECG and PCG signals. These signals are introduced and their characteristics are explained. Then, using PCG signal as the reference, the Gaussian process modeling is employed to provide the possibility of flexible models as nonlinear estimations. The method also tries to facilitate the practical implementation of the device by using the less possible number of channels and also by using only 1-bit reference signal.The method is tested on synthetic data and also on real data that is recorded to provide a synchronous multi-modal data set.Since a standard agreement for the acquisition of these modalities is not yet taken into much consideration, the factors which influence the signals in recording procedure are introduced and their difficulties and effects are investigated.The results show that the multi-modal approach is efficient in the detection of R-peaks and so in the extraction of fetal heart rate, and it also provides the results about the morphology of fetal ECG
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Bücher zum Thema "ECG extraction"

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Hu, Li, und Zhiguo Zhang, Hrsg. EEG Signal Processing and Feature Extraction. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2.

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Blakemore, Robert John. The determination of change in PCB concentration by GC/MS with Soxhlet extraction: And GC/ECD with automated solvent extraction from Portsmouth Harbour sediment. Portsmouth: University of Portsmouth, School of Pharmacy and Biomedical Sciences, 1999.

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Lee, Jimmy Kim-Mil. ECG feature extraction without fiducial detection: Applications to ECG biometric recognition. 2006.

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Hu, Li, und Zhiguo Zhang. EEG Signal Processing and Feature Extraction. Springer, 2019.

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Leong, Wai Yie. EEG Signal Processing: Feature extraction, selection and classification methods. The Institution of Engineering and Technology, 2019.

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Wai Yie Leong, Hrsg. EEG Signal Processing: Feature extraction, selection and classification methods. Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/pbhe016e.

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Hancock, Kathleen J., und Juliann Emmons Allison, Hrsg. The Oxford Handbook of Energy Politics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190861360.001.0001.

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In many ways, everything we once knew about energy resources and technologies has been impacted by: the longstanding scientific consensus on climate change and related support for renewable energy; the affordability of extraction of unconventional fuels; increasing demand for energy resources by middle- and low-income nations; new regional and global stakeholders; fossil fuel discoveries and emerging renewable technologies; awareness of (trans)local politics; and rising interest in corporate social responsibility (CSR) and the need for energy justice. Research on these and related topics now appears frequently in social science academic journals in broad-based journals, such as International Organization, International Studies Quarterly, and Review of International Political Economy, as well as those focused specifically on energy (e.g., Energy Research & Social Science and Energy Policy), the environment (Global Environmental Politics), natural resources (Resources Policy), and extractive industries (Extractive Industries and Society). The Oxford Handbook of Energy Politics synthesizes and aggregates this substantively diverse literature to provide insights into, and a foundation for teaching and research on, critical energy issues primarily in the areas of international relations and comparative politics. Its primary goals are to further develop the energy politics scholarship and community, and generate sophisticated new work that will benefit a variety of scholars working on energy issues.
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Kumar, Nilesh, Nilesh Ramesh Kulkarni und Vinayak Bairagi. EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques. Elsevier Science & Technology Books, 2018.

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Prasad, Girijesh. Brain–machine interfaces. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0049.

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A brain–machine interface (BMI) is a biohybrid system intended as an alternative communication channel for people suffering from severe motor impairments. A BMI can involve either invasively implanted electrodes or non-invasive imaging systems. The focus in this chapter is on non-invasive approaches; EEG-based BMI is the most widely investigated. Event-related de-synchronization/ synchronization (ERD/ERS) of sensorimotor rhythms (SMRs), P300, and steady-state visual evoked potential (SSVEP) are the three main cortical activation patterns used for designing an EEG-based BMI. A BMI involves multiple stages: brain data acquisition, pre-processing, feature extraction, and feature classification, along with a device to communicate or control with or without neurofeedback. Despite extensive research worldwide, there are still several challenges to be overcome in making BMI practical for daily use. One such is to account for non-stationary brainwaves dynamics. Also, some people may initially find it difficult to establish a reliable BMI with sufficient accuracy. BMI research, however, is progressing in two broad areas: replacing neuromuscular pathways and neurorehabilitation.
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Baker, Maria, Eva Ramirez-Llodra und Paul Tyler, Hrsg. Natural Capital and Exploitation of the Deep Ocean. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198841654.001.0001.

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The deep ocean is, by far, the planet’s largest biome and holds a wealth of potential natural assets. Most of the ocean lies beyond national jurisdiction and hence is the responsibility of us all. Human exploitation of the deep ocean is rapidly increasing, becoming more visible to many through the popular media. The scientific literature of deep-sea exploitation and its actual and potential effects has also rapidly expanded as a direct function of this increased national and global interest in deep-sea resources, both biological (e.g. fisheries, genetic resources) and non-biological (e.g. minerals, oil, gas, methane hydrate). At the same time there is a growing interest in deep-sea contamination (including plastics), with many such studies featured in high-profile scientific journals and covered by global media outlets. Finally, climate change is affecting even the deepest regions of our oceans and is a major priority for the international scientific and political agendas. However, there is currently no comprehensive integration of information about resource extraction, pollution and effects of climate change and these topics are only superficially covered in classic textbooks on deep-sea biology. The human race is at a pivotal point in potentially benefitting from the deep ocean’s natural resources and this concise and accessible work provides an account of past explorations and exploitations of the deep ocean, a present understanding of its natural capital and how this may be exploited sustainably for the benefit of humankind whilst maintaining its ecological integrity. The book gives a comprehensive account of geological and physical processes, ecology and biology, exploitation, management, and conservation.
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Buchteile zum Thema "ECG extraction"

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Li, Dong, Kai Huang, Hanlin Zhang und Liqing Zhang. „UMPCA Based Feature Extraction for ECG“. In Advances in Neural Networks – ISNN 2013, 383–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39065-4_47.

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Wang, Jian, Yanwei Pang, Yuqing He und Jing Pan. „ECG Waveform Extraction from Paper Records“. In Lecture Notes in Computer Science, 505–12. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71589-6_44.

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Dong, Kejun, Li Zhao und Chengyu Liu. „Respiratory Signal Extraction from ECG Signal“. In Feature Engineering and Computational Intelligence in ECG Monitoring, 227–43. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3824-7_13.

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Luo, Zhongliang, Jingguo Dai und Zhuohua Duan. „The Comparison of Fetal ECG Extraction Methods“. In Lecture Notes in Electrical Engineering, 3469–74. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7618-0_452.

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Nair, Mahesh A. „ECG Feature Extraction using Time Frequency Analysis“. In Innovations in Computing Sciences and Software Engineering, 461–66. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9112-3_78.

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Kaleem, Abdullah Mohammed, und Rajendra D. Kokate. „Performance Evaluation of Fetal ECG Extraction Algorithms“. In Lecture Notes in Electrical Engineering, 187–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5802-9_17.

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Lamesgin, Gizeaddis, Yonas Kassaw und Dawit Assefa. „Extraction of Fetal ECG from Abdominal ECG and Heart Rate Variability Analysis“. In Advances in Intelligent Systems and Computing, 65–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13572-4_5.

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8

Kuzilek, Jakub, Lenka Lhotska und Michal Huptych. „Extraction of beats from noisy ECG using ICA“. In IFMBE Proceedings, 469–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29305-4_124.

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9

P., Anita, und K. T. Talele. „ECG Feature Extraction Using Wavelet Based Derivative Approach“. In Communications in Computer and Information Science, 239–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20209-4_34.

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German-Sallo, Z. „Efficient ECG Signal Parameters Extraction Using Multiresolution Analysis“. In IFMBE Proceedings, 227–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04292-8_50.

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Konferenzberichte zum Thema "ECG extraction"

1

Serdengecti, Cigdem, Mehmet Engin, Erkan Zeki Engin und Soner Balci. „Extraction of fetal ECG from maternal ECG“. In 2009 14th National Biomedical Engineering Meeting. IEEE, 2009. http://dx.doi.org/10.1109/biyomut.2009.5130355.

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2

Tekeste, Temesghen, Nourhan Bayasi, Hani Saleh, Ahsan Khandoker, Baker Mohammad, Mahmoud Al-Qutayri und Mohammed Ismail. „Adaptive ECG interval extraction“. In 2015 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2015. http://dx.doi.org/10.1109/iscas.2015.7168804.

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3

Gualsaqui Miranda, Marco V., Ivan P. Vizcaino Espinosa und Marco J. Flores Calero. „ECG signal features extraction“. In 2016 IEEE Ecuador Technical Chapters Meeting (ETCM). IEEE, 2016. http://dx.doi.org/10.1109/etcm.2016.7750859.

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4

Anisha, M., S. S. Kumar und M. Benisha. „Survey on Fetal ECG extraction“. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, 2014. http://dx.doi.org/10.1109/iccicct.2014.6993123.

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5

Sutar, Rajendra G., A. G. Kothari und A. G. Keskar. „ECG Feature Extraction Using LCAD“. In 2012 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2012. http://dx.doi.org/10.1109/csnt.2012.31.

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6

Peshave, Juie D., und Rajveer Shastri. „Feature extraction of ECG signal“. In 2014 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2014. http://dx.doi.org/10.1109/iccsp.2014.6950168.

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7

Bhoraniya, Dixit V., und Rahul K. Kher. „Motion artifacts extraction using dwt from ambulatory ECG (A-ECG)“. In 2014 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2014. http://dx.doi.org/10.1109/iccsp.2014.6950112.

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8

Rajesh, A. V., und R. Ganesan. „Comprehensive study on fetal ECG extraction“. In 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). IEEE, 2014. http://dx.doi.org/10.1109/iccicct.2014.6993141.

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9

Hassanpour, Hamid, und Amin Parsaei. „Fetal ECG Extraction Using Wavelet Transform“. In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06). IEEE, 2006. http://dx.doi.org/10.1109/cimca.2006.98.

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10

Espiritu-Santo-Rincon, Antonio, und Cuauhtemoc Carbajal-Fernandez. „ECG feature extraction via waveform segmentation“. In 2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) (Formerly known as ICEEE). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5608655.

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