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Auswahl der wissenschaftlichen Literatur zum Thema „Classification of biomedical time series“
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Zeitschriftenartikel zum Thema "Classification of biomedical time series"
Ramanujam, E., und S. Padmavathi. „Genetic time series motif discovery for time series classification“. International Journal of Biomedical Engineering and Technology 31, Nr. 1 (2019): 47. http://dx.doi.org/10.1504/ijbet.2019.101051.
Der volle Inhalt der QuelleJin, Lin-peng, und Jun Dong. „Ensemble Deep Learning for Biomedical Time Series Classification“. Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/6212684.
Der volle Inhalt der QuelleIvaturi, Praharsh, Matteo Gadaleta, Amitabh C. Pandey, Michael Pazzani, Steven R. Steinhubl und Giorgio Quer. „A Comprehensive Explanation Framework for Biomedical Time Series Classification“. IEEE Journal of Biomedical and Health Informatics 25, Nr. 7 (Juli 2021): 2398–408. http://dx.doi.org/10.1109/jbhi.2021.3060997.
Der volle Inhalt der QuelleWang, Jin, Ping Liu, Mary F. H. She, Saeid Nahavandi und Abbas Kouzani. „Bag-of-words representation for biomedical time series classification“. Biomedical Signal Processing and Control 8, Nr. 6 (November 2013): 634–44. http://dx.doi.org/10.1016/j.bspc.2013.06.004.
Der volle Inhalt der QuelleKu-Maldonado, Carlos Alejandro, und Erik Molino-Minero-Re. „Performance Evaluation of Biomedical Time Series Transformation Methods for Classification Tasks“. Revista Mexicana de Ingeniería Biomédica 44, Nr. 4 (17.08.2023): 105–16. http://dx.doi.org/10.17488/rmib.44.4.7.
Der volle Inhalt der QuelleGupta, R., A. Mittal, K. Singh, V. Narang und S. Roy. „Time-series approach to protein classification problem“. IEEE Engineering in Medicine and Biology Magazine 28, Nr. 4 (Juli 2009): 32–37. http://dx.doi.org/10.1109/memb.2009.932903.
Der volle Inhalt der QuelleWang, Will Ke, Ina Chen, Leeor Hershkovich, Jiamu Yang, Ayush Shetty, Geetika Singh, Yihang Jiang et al. „A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications“. Sensors 22, Nr. 20 (20.10.2022): 8016. http://dx.doi.org/10.3390/s22208016.
Der volle Inhalt der QuelleLemus, Mariano, João P. Beirão, Nikola Paunković, Alexandra M. Carvalho und Paulo Mateus. „Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data“. Entropy 22, Nr. 1 (30.12.2019): 49. http://dx.doi.org/10.3390/e22010049.
Der volle Inhalt der QuelleAthavale, Yashodhan, Sridhar Krishnan und Aziz Guergachi. „Pattern Classification of Signals Using Fisher Kernels“. Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/467175.
Der volle Inhalt der QuelleCarreiro, André V., Orlando Anunciação, João A. Carriço und Sara C. Madeira. „Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series“. Journal of Integrative Bioinformatics 8, Nr. 3 (01.12.2011): 73–89. http://dx.doi.org/10.1515/jib-2011-175.
Der volle Inhalt der QuelleDissertationen zum Thema "Classification of biomedical time series"
Rajan, Jebu Jacob. „Time series classification“. Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339538.
Der volle Inhalt der QuelleMatam, Basava R. „Watermarking biomedical time series data“. Thesis, Aston University, 2009. http://publications.aston.ac.uk/15351/.
Der volle Inhalt der QuelleKhessiba, Souhir. „Stratégies d’optimisation des hyper-paramètres de réseaux de neurones appliqués aux signaux temporels biomédicaux“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAE003.
Der volle Inhalt der QuelleThis thesis focuses on optimizing the hyperparameters of convolutional neural networks (CNNs) in the medical domain, proposing an innovative approach to improve the performance of decision-making models in the biomedical field. Through the use of a hybrid approach, GS-TPE, to effectively adjust the hyperparameters of complex neural network models, this research has demonstrated significant improvements in the classification of temporal biomedical signals, such as vigilance states, from physiological signals such as electroencephalogram (EEG). Furthermore, by introducing a new DNN architecture, STGCN, for the classification of gestures associated with pathologies such as knee osteoarthritis and Parkinson's disease from video gait analysis, these works offer new perspectives for enhancing medical diagnosis and management through advancements in artificial intelligence
Al-Wasel, Ibrahim A. „Spectral analysis for replicated biomedical time series“. Thesis, Lancaster University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412585.
Der volle Inhalt der QuelleEsling, Philippe. „Multiobjective time series matching and classification“. Paris 6, 2012. http://www.theses.fr/2012PA066704.
Der volle Inhalt der QuelleMillions of years of genetic evolution have shaped our auditory system, allowing to discriminate acoustic events in a flexible manner. We can perceptually process multiple de-correlated scales in a multidimensional way. In addition, humans have a natural ability to extract a coherent structure from temporal shapes. We show that emulating these mechanisms in our algorithmic choices, allow to create efficient approaches to perform matching and classification, with a scope beyond musical issues. We introduce the problem of multiobjective Time Series (MOTS) and propose an efficient algorithm to solve it. We introduce two innovative querying paradigms on audio files. We introduce a new classification paradigm based on the hypervolume dominated by different classes called hypervolume-MOTS (HV-MOTS). This system studies the behavior of the whole class by its distribution and spread over the optimization space. We show an improvement over the state of the art methods on a wide range of scientific problems. We present a biometric identification systems based on the sounds produced by heartbeats. This system is able to reach low error rates equivalent to other biometric features. These results are confirmed by the extensive cardiac data set of the Mars500 isolation study. Finally, we study the problem of generating orchestral mixtures that could best imitate a sound target. The search algorithm based on MOTS problem allows to obtain a set of solutions to approximate any audio source
Nunes, Neuza Filipa Martins. „Algorithms for time series clustering applied to biomedical signals“. Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5666.
Der volle Inhalt der QuelleThe increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the user, has a significant value in the biosignal’s processing field. The present dissertation introduces new algorithms for time series clustering, where we are able to separate and organize unlabeled data into different groups whose signals are similar to each other. Signal processing algorithms were developed for the detection of a meanwave, which represents the signal’s morphology and behavior. The algorithm designed computes the meanwave by separating and averaging all cycles of a cyclic continuous signal. To increase the quality of information given by the meanwave, a set of wave-alignment techniques was also developed and its relevance was evaluated in a real database. To evaluate our algorithm’s applicability in time series clustering, a distance metric created with the information of the automatic meanwave was designed and its measurements were given as input to a K-Means clustering algorithm. With that purpose, we collected a series of data with two different modes in it. The produced algorithm successfully separates two modes in the collected data with 99.3% of efficiency. The results of this clustering procedure were compared to a mechanism widely used in this area, which models the data and uses the distance between its cepstral coefficients to measure the similarity between the time series.The algorithms were also validated in different study projects. These projects show the variety of contexts in which our algorithms have high applicability and are suitable answers to overcome the problems of exhaustive signal analysis and expert intervention. The algorithms produced are signal-independent, and therefore can be applied to any type of signal providing it is a cyclic signal. The fact that this approach doesn’t require any prior information and the preliminary good performance make these algorithms powerful tools for biosignals analysis and classification.
Lines, Jason. „Time Series classification through transformation and ensembles“. Thesis, University of East Anglia, 2015. https://ueaeprints.uea.ac.uk/53360/.
Der volle Inhalt der QuelleGhalwash, Mohamed. „Interpretable Early Classification of Multivariate Time Series“. Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/239730.
Der volle Inhalt der QuellePh.D.
Recent advances in technology have led to an explosion in data collection over time rather than in a single snapshot. For example, microarray technology allows us to measure gene expression levels in different conditions over time. Such temporal data grants the opportunity for data miners to develop algorithms to address domain-related problems, e.g. a time series of several different classes can be created, by observing various patient attributes over time and the task is to classify unseen patient based on his temporal observations. In time-sensitive applications such as medical applications, some certain aspects have to be considered besides providing accurate classification. The first aspect is providing early classification. Accurate and timely diagnosis is essential for allowing physicians to design appropriate therapeutic strategies at early stages of diseases, when therapies are usually the most effective and the least costly. We propose a probabilistic hybrid method that allows for early, accurate, and patient-specific classification of multivariate time series that, by training on a full time series, offer classification at a very early time point during the diagnosis phase, while staying competitive in terms of accuracy with other models that use full time series both in training and testing. The method has attained very promising results and outperformed the baseline models on a dataset of response to drug therapy in Multiple Sclerosis patients and on a sepsis therapy dataset. Although attaining accurate classification is the primary goal of data mining task, in medical applications it is important to attain decisions that are not only accurate and obtained early, but can also be easily interpreted which is the second aspect of medical applications. Physicians tend to prefer interpretable methods rather than black-box methods. For that purpose, we propose interpretable methods for early classification by extracting interpretable patterns from the raw time series to help physicians in providing early diagnosis and to gain insights into and be convinced about the classification results. The proposed methods have been shown to be more accurate and provided classifications earlier than three alternative state-of-the-art methods when evaluated on human viral infection datasets and a larger myocardial infarction dataset. The third aspect has to be considered for medical applications is the need for predictions to be accompanied by a measure which allows physicians to judge about the uncertainty or belief in the prediction. Knowing the uncertainty associated with a given prediction is especially important in clinical diagnosis where data mining methods assist clinical experts in making decisions and optimizing therapy. We propose an effective method to provide uncertainty estimate for the proposed interpretable early classification methods. The method was evaluated on four challenging medical applications by characterizing decrease in uncertainty of prediction. We showed that our proposed method meets the requirements of uncertainty estimates (the proposed uncertainty measure takes values in the range [0,1] and propagates over time). To the best of our knowledge, this PhD thesis will have a great impact on the link between data mining community and medical domain experts and would give physicians sufficient confidence to put the proposed methods into real practice.
Temple University--Theses
Owsley, Lane M. D. „Classification of transient events in time series /“. Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/5989.
Der volle Inhalt der QuelleBotsch, Michael-Felix. „Machine learning techniques for time series classification“. Göttingen Cuvillier, 2009. http://d-nb.info/994721455/04.
Der volle Inhalt der QuelleBücher zum Thema "Classification of biomedical time series"
Melin, Patricia, Martha Ramirez und Oscar Castillo. Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71101-5.
Der volle Inhalt der QuelleAbel, Jaison R. A time series and cross-sectional classification of state regulatory policy adopted for local exchange carriers: Divestiture to present, 1984-1998. Columbus, Ohio: National Regulatory Research Institute, 1998.
Den vollen Inhalt der Quelle findenTime Series Clustering and Classification. Chapman and Hall/CRC, 2019.
Den vollen Inhalt der Quelle findenMaharaj, Elizabeth Ann, Pierpaolo D'Urso und Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenMaharaj, Elizabeth Ann, Pierpaolo D'Urso und Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenMaharaj, Elizabeth Ann, Pierpaolo D'Urso und Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenMaharaj, Elizabeth Ann, Pierpaolo D'Urso und Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenMaharaj, Elizabeth Ann, Jorge Caiado und Pierpaolo DUrso. Time Series Clustering and Classification. Taylor & Francis Group, 2021.
Den vollen Inhalt der Quelle findenBuza, Krisztian. Fusion Methods for Time-Series Classification. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2011.
Den vollen Inhalt der Quelle findenVolna, Eva, Martin Kotyrba und Michal Janosek. Pattern Recognition and Classification in Time Series Data. IGI Global, 2016.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Classification of biomedical time series"
Jović, Alan, Karla Brkić und Nikola Bogunović. „Decision Tree Ensembles in Biomedical Time-Series Classification“. In Lecture Notes in Computer Science, 408–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32717-9_41.
Der volle Inhalt der QuelleBock, Christian, Michael Moor, Catherine R. Jutzeler und Karsten Borgwardt. „Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning“. In Methods in Molecular Biology, 33–71. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0826-5_2.
Der volle Inhalt der QuelleAbid, M., Y. Ouakrim, A. Mitiche, P. A. Vendittoli, N. Hagemeister und N. Mezghani. „A Comparative Study of End-To-End Discriminative Deep Learning Models for Knee Joint Kinematic Time Series Classification“. In Biomedical Signal Processing, 33–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-67494-6_2.
Der volle Inhalt der QuelleAcerbi, Enzo, Caroline Chénard, Stephan C. Schuster und Federico M. Lauro. „Discovering Trends in Environmental Time-Series with Supervised Classification of Metatranscriptomic Reads and Empirical Mode Decomposition“. In Biomedical Engineering Systems and Technologies, 192–210. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29196-9_11.
Der volle Inhalt der QuelleDe, Bikram, Mykhailo Sakevych und Vangelis Metsis. „The Impact of Data Augmentation on Time Series Classification Models: An In-Depth Study with Biomedical Data“. In Artificial Intelligence in Medicine, 192–203. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66538-7_20.
Der volle Inhalt der QuelleGrabocka, Josif, Alexandros Nanopoulos und Lars Schmidt-Thieme. „Invariant Time-Series Classification“. In Machine Learning and Knowledge Discovery in Databases, 725–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33486-3_46.
Der volle Inhalt der QuelleKotsifakos, Alexios, und Panagiotis Papapetrou. „Model-Based Time Series Classification“. In Advances in Intelligent Data Analysis XIII, 179–91. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12571-8_16.
Der volle Inhalt der QuelleRoychoudhury, Shoumik, Mohamed Ghalwash und Zoran Obradovic. „Cost Sensitive Time-Series Classification“. In Machine Learning and Knowledge Discovery in Databases, 495–511. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_30.
Der volle Inhalt der QuelleCamiz, Sergio. „Exploratory Classification of Time-Series“. In Handbook of Research on Emerging Theories, Models, and Applications of Financial Econometrics, 1–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54108-8_1.
Der volle Inhalt der QuelleHong, Yi, Yundi Shi, Martin Styner, Mar Sanchez und Marc Niethammer. „Simple Geodesic Regression for Image Time-Series“. In Biomedical Image Registration, 11–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31340-0_2.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Classification of biomedical time series"
Kim, Boah, Tejas Sudharshan Mathai, Kimberly Helm und Ronald M. Summers. „Automated Classification of Multi-Parametric Body MRI Series“. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635686.
Der volle Inhalt der QuelleCasella, Bruno, Matthias Jakobs, Marco Aldinucci und Sebastian Buschjäger. „Federated Time Series Classification with ROCKET features“. In ESANN 2024, 87–92. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-61.
Der volle Inhalt der QuelleFong, Simon, Kun Lan, Paul Sun, Sabah Mohammed und Jinan Fiaidhi. „A Time-Series Pre-Processing Methodology for Biosignal Classification using Statistical Feature Extraction“. In Biomedical Engineering. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.791-100.
Der volle Inhalt der QuelleMatarmaa, Jarno, und Anton Dolganov. „Sport Activity Classification Using Interlaced Multivariate Time Series Signals“. In 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). IEEE, 2023. http://dx.doi.org/10.1109/usbereit58508.2023.10158886.
Der volle Inhalt der QuelleWang, Qian, Zhenguo Zhang und Rongyi Cui. „Classification-oriented Feature Extraction from Time Series by Comparing Learning“. In 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2019. http://dx.doi.org/10.1109/cisp-bmei48845.2019.8965685.
Der volle Inhalt der QuelleMure, Simon, Thomas Grenier, Charles R. G. Guttmann, Francois Cotton und Hugues Benoit-Cattin. „Classification of multiple sclerosis lesion evolution patterns a study based on unsupervised clustering of asynchronous time-series“. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016). IEEE, 2016. http://dx.doi.org/10.1109/isbi.2016.7493509.
Der volle Inhalt der QuelleMbouopda, Michael Franklin. „Uncertain Time Series Classification“. In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/683.
Der volle Inhalt der QuelleKrawczak, Maciej, und Grazyna Szkatula. „Time series envelopes for classification“. In 2010 5th IEEE International Conference Intelligent Systems (IS). IEEE, 2010. http://dx.doi.org/10.1109/is.2010.5548371.
Der volle Inhalt der QuelleLi, Sheng, Yaliang Li und Yun Fu. „Multi-View Time Series Classification“. In CIKM'16: ACM Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2983323.2983780.
Der volle Inhalt der QuelleHsieh, Tsung-Yu, Suhang Wang, Yiwei Sun und Vasant Honavar. „Explainable Multivariate Time Series Classification“. In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441815.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Classification of biomedical time series"
Gupta, Maya R., Nathan Parrish und Hyrum S. Anderson. Early time-series classification with reliability guarantee. Office of Scientific and Technical Information (OSTI), August 2012. http://dx.doi.org/10.2172/1051704.
Der volle Inhalt der QuelleSinkovits, Robert. Optimization and Parallelization of a Time Series Classification Algorithm. Extreme Science and Engineering Discovery Environment (XSEDE), August 2019. http://dx.doi.org/10.21900/xsede-2019.1.
Der volle Inhalt der QuelleSchryver, J. C., und N. Rao. Classification of time series patterns from complex dynamic systems. Office of Scientific and Technical Information (OSTI), Juli 1998. http://dx.doi.org/10.2172/663242.
Der volle Inhalt der QuelleDanvers, Alexander, Evan Carter, Matthias Mehl und Esther Sternberg. Time-Series Classification for Predicting Self-Reported Job Performance. Aberdeen Proving Ground, MD: DEVCOM Army Research Laboratory, November 2021. http://dx.doi.org/10.21236/ad1153640.
Der volle Inhalt der QuelleSenin, Pavel, und Sergey Malinchik. SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model. Fort Belvoir, VA: Defense Technical Information Center, Januar 2013. http://dx.doi.org/10.21236/ada603196.
Der volle Inhalt der QuelleLasko, Kristofer, Francis O’Neill und Elena Sava. Automated mapping of land cover type within international heterogenous landscapes using Sentinel-2 imagery with ancillary geospatial data. Engineer Research and Development Center (U.S.), September 2024. http://dx.doi.org/10.21079/11681/49367.
Der volle Inhalt der QuelleArévalo-Sáenz, Alejandra, Borja Ferrández Pujante und Fernando J. Rascón-Ramírez. Peritumoral Edema in Resected Meningiomas: Study of Factors Associated with the Variability of Postoperative Duration. Science Repository, März 2024. http://dx.doi.org/10.31487/j.scr.2024.01.05.
Der volle Inhalt der QuelleBerney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman und John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40401.
Der volle Inhalt der QuelleResearch, IFF. FSA and Official Controls: Research with Food Business Operators. Food Standards Agency, Februar 2023. http://dx.doi.org/10.46756/sci.fsa.drn484.
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