Academic literature on the topic 'Classification of biomedical time series'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Classification of biomedical time series.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Classification of biomedical time series"
Ramanujam, E., and S. Padmavathi. "Genetic time series motif discovery for time series classification." International Journal of Biomedical Engineering and Technology 31, no. 1 (2019): 47. http://dx.doi.org/10.1504/ijbet.2019.101051.
Full textJin, Lin-peng, and 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.
Full textIvaturi, Praharsh, Matteo Gadaleta, Amitabh C. Pandey, Michael Pazzani, Steven R. Steinhubl, and Giorgio Quer. "A Comprehensive Explanation Framework for Biomedical Time Series Classification." IEEE Journal of Biomedical and Health Informatics 25, no. 7 (July 2021): 2398–408. http://dx.doi.org/10.1109/jbhi.2021.3060997.
Full textWang, Jin, Ping Liu, Mary F. H. She, Saeid Nahavandi, and Abbas Kouzani. "Bag-of-words representation for biomedical time series classification." Biomedical Signal Processing and Control 8, no. 6 (November 2013): 634–44. http://dx.doi.org/10.1016/j.bspc.2013.06.004.
Full textKu-Maldonado, Carlos Alejandro, and Erik Molino-Minero-Re. "Performance Evaluation of Biomedical Time Series Transformation Methods for Classification Tasks." Revista Mexicana de Ingeniería Biomédica 44, no. 4 (August 17, 2023): 105–16. http://dx.doi.org/10.17488/rmib.44.4.7.
Full textGupta, R., A. Mittal, K. Singh, V. Narang, and S. Roy. "Time-series approach to protein classification problem." IEEE Engineering in Medicine and Biology Magazine 28, no. 4 (July 2009): 32–37. http://dx.doi.org/10.1109/memb.2009.932903.
Full textWang, 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, no. 20 (October 20, 2022): 8016. http://dx.doi.org/10.3390/s22208016.
Full textLemus, Mariano, João P. Beirão, Nikola Paunković, Alexandra M. Carvalho, and Paulo Mateus. "Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data." Entropy 22, no. 1 (December 30, 2019): 49. http://dx.doi.org/10.3390/e22010049.
Full textAthavale, Yashodhan, Sridhar Krishnan, and 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.
Full textCarreiro, André V., Orlando Anunciação, João A. Carriço, and Sara C. Madeira. "Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series." Journal of Integrative Bioinformatics 8, no. 3 (December 1, 2011): 73–89. http://dx.doi.org/10.1515/jib-2011-175.
Full textDissertations / Theses on the topic "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.
Full textMatam, Basava R. "Watermarking biomedical time series data." Thesis, Aston University, 2009. http://publications.aston.ac.uk/15351/.
Full textKhessiba, 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.
Full textThis 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.
Full textEsling, Philippe. "Multiobjective time series matching and classification." Paris 6, 2012. http://www.theses.fr/2012PA066704.
Full textMillions 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.
Full textThe 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/.
Full textGhalwash, Mohamed. "Interpretable Early Classification of Multivariate Time Series." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/239730.
Full textPh.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.
Full textBotsch, Michael-Felix. "Machine learning techniques for time series classification." Göttingen Cuvillier, 2009. http://d-nb.info/994721455/04.
Full textBooks on the topic "Classification of biomedical time series"
Melin, Patricia, Martha Ramirez, and 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.
Full textAbel, 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.
Find full textTime Series Clustering and Classification. Chapman and Hall/CRC, 2019.
Find full textMaharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.
Find full textMaharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.
Find full textMaharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.
Find full textMaharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.
Find full textMaharaj, Elizabeth Ann, Jorge Caiado, and Pierpaolo DUrso. Time Series Clustering and Classification. Taylor & Francis Group, 2021.
Find full textBuza, Krisztian. Fusion Methods for Time-Series Classification. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2011.
Find full textVolna, Eva, Martin Kotyrba, and Michal Janosek. Pattern Recognition and Classification in Time Series Data. IGI Global, 2016.
Find full textBook chapters on the topic "Classification of biomedical time series"
Jović, Alan, Karla Brkić, and 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.
Full textBock, Christian, Michael Moor, Catherine R. Jutzeler, and 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.
Full textAbid, M., Y. Ouakrim, A. Mitiche, P. A. Vendittoli, N. Hagemeister, and 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.
Full textAcerbi, Enzo, Caroline Chénard, Stephan C. Schuster, and 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.
Full textDe, Bikram, Mykhailo Sakevych, and 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.
Full textGrabocka, Josif, Alexandros Nanopoulos, and 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.
Full textKotsifakos, Alexios, and 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.
Full textRoychoudhury, Shoumik, Mohamed Ghalwash, and 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.
Full textCamiz, 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.
Full textHong, Yi, Yundi Shi, Martin Styner, Mar Sanchez, and 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.
Full textConference papers on the topic "Classification of biomedical time series"
Kim, Boah, Tejas Sudharshan Mathai, Kimberly Helm, and 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.
Full textCasella, Bruno, Matthias Jakobs, Marco Aldinucci, and 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.
Full textFong, Simon, Kun Lan, Paul Sun, Sabah Mohammed, and 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.
Full textMatarmaa, Jarno, and 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.
Full textWang, Qian, Zhenguo Zhang, and 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.
Full textMure, Simon, Thomas Grenier, Charles R. G. Guttmann, Francois Cotton, and 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.
Full textMbouopda, 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.
Full textKrawczak, Maciej, and 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.
Full textLi, Sheng, Yaliang Li, and 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.
Full textHsieh, Tsung-Yu, Suhang Wang, Yiwei Sun, and 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.
Full textReports on the topic "Classification of biomedical time series"
Gupta, Maya R., Nathan Parrish, and 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.
Full textSinkovits, 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.
Full textSchryver, J. C., and N. Rao. Classification of time series patterns from complex dynamic systems. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/663242.
Full textDanvers, Alexander, Evan Carter, Matthias Mehl, and 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.
Full textSenin, Pavel, and Sergey Malinchik. SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada603196.
Full textLasko, Kristofer, Francis O’Neill, and 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.
Full textArévalo-Sáenz, Alejandra, Borja Ferrández Pujante, and Fernando J. Rascón-Ramírez. Peritumoral Edema in Resected Meningiomas: Study of Factors Associated with the Variability of Postoperative Duration. Science Repository, March 2024. http://dx.doi.org/10.31487/j.scr.2024.01.05.
Full textBerney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and 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.
Full textResearch, IFF. FSA and Official Controls: Research with Food Business Operators. Food Standards Agency, February 2023. http://dx.doi.org/10.46756/sci.fsa.drn484.
Full text