Готові списки джерел за темами / Classification of biomedical time series

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Добірка наукової літератури з теми "Classification of biomedical time series"

Автор: Grafiati
Опубліковано: 21 грудня 2024

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Статті в журналах з теми "Classification of biomedical time series"

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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.

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Jin, 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.

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Анотація:
Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such asBaggingandAdaBoost.
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Ivaturi, 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.

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Wang, 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.

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Ku-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.

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The extraction of time series features is essential across various fields, yet it remains a challenging endeavor. Therefore, it's crucial to identify appropriate methods capable of extracting pertinent information that can significantly enhance classification performance. Among these methods are those that translate time series into different domains. This study investigates three distinct time series transformation approaches for addressing time series classification challenges within biomedical data. The first method involves a response vector transformation, while the other two employ image transformation techniques: RandOm Convolutional KErnel Transform (ROCKET), Gramian Angular Fields, and Markov Transition Fields. These transformation methods were applied to five biomedical datasets, exploring various format configurations to ascertain the optimal representation technique and configuration for input, which in turn improves classification performance. Evaluations were conducted on the effectiveness of these methods in conjunction with two classification algorithms. The outcomes underscore the significance of these time series transformation techniques as facilitators for enhanced classification algorithms documented in current literature.
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Gupta, 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.

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Wang, 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.

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Анотація:
Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.
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Lemus, 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.

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Анотація:
Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to obtain information in advance by having early knowledge about a specific event may be of great utility in many areas. Early classification arises as an extension of the time-series classification problem, given the need to obtain a reliable prediction as soon as possible. In this work, we propose an information-theoretic method, named Multivariate Correlations for Early Classification (MCEC), to characterize the early classification opportunity of a time-series. Experimental validation is performed on synthetic and benchmark data, confirming the ability of the MCEC algorithm to perform a trade-off between accuracy and earliness in a wide-spectrum of time-series data, such as those collected from sensors, images, spectrographs, and electrocardiograms.
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Athavale, 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.

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The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals. Our research work has indicated that Fisher kernels have shown substantial improvement in signal classification by enabling clearer pattern visualization in three-dimensional space. In this paper, we will exhibit the performance of Fisher kernels for two domains: financial and biomedical. The financial domain study involves identifying the possibility of collapse or survival of a company trading in the stock market. For assessing the fate of each company, we have collected financial time-series composed of weekly closing stock prices in a common time frame, using Thomson Datastream software. The biomedical domain study involves knee signals collected using the vibration arthrometry technique. This study uses the severity of cartilage degeneration for classifying normal and abnormal knee joints. In both studies, we apply Fisher Kernels incorporated with a Gaussian mixture model (GMM) for dimension transformation into feature space, which is created as a three-dimensional plot for visualization and for further classification using support vector machines. From our experiments we observe that Fisher Kernel usage fits really well for both kinds of signals, with low classification error rates.
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Carreiro, 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.

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Summary The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perspective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient’s response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series.
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Дисертації з теми "Classification of biomedical time series"

1

Rajan, Jebu Jacob. "Time series classification." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339538.

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Matam, Basava R. "Watermarking biomedical time series data." Thesis, Aston University, 2009. http://publications.aston.ac.uk/15351/.

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This thesis addresses the problem of information hiding in low dimensional digital data focussing on issues of privacy and security in Electronic Patient Health Records (EPHRs). The thesis proposes a new security protocol based on data hiding techniques for EPHRs. This thesis contends that embedding of sensitive patient information inside the EPHR is the most appropriate solution currently available to resolve the issues of security in EPHRs. Watermarking techniques are applied to one-dimensional time series data such as the electroencephalogram (EEG) to show that they add a level of confidence (in terms of privacy and security) in an individual’s diverse bio-profile (the digital fingerprint of an individual’s medical history), ensure belief that the data being analysed does indeed belong to the correct person, and also that it is not being accessed by unauthorised personnel. Embedding information inside single channel biomedical time series data is more difficult than the standard application for images due to the reduced redundancy. A data hiding approach which has an in built capability to protect against illegal data snooping is developed. The capability of this secure method is enhanced by embedding not just a single message but multiple messages into an example one-dimensional EEG signal. Embedding multiple messages of similar characteristics, for example identities of clinicians accessing the medical record helps in creating a log of access while embedding multiple messages of dissimilar characteristics into an EPHR enhances confidence in the use of the EPHR. The novel method of embedding multiple messages of both similar and dissimilar characteristics into a single channel EEG demonstrated in this thesis shows how this embedding of data boosts the implementation and use of the EPHR securely.
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3

Khessiba, 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.

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Cette thèse est axée sur l'optimisation des hyperparamètres des réseaux de neurones à convolution (CNN) dans le domaine médical, proposant une approche innovante visant à améliorer la performance des modèles décisionnels dans le domaine biomédical. Grâce à l'utilisation d'une approche hybride, GS-TPE, pour ajuster efficacement les hyperparamètres des modèles de réseaux de neurones complexes , cette recherche a démontré des améliorations significatives dans la classification des signaux biomédicaux temporels, à savoir les états de vigilance, à partir de signaux physiologiques tels que l'électroencéphalogramme (EEG). De plus, grâce à l'introduction d'une nouvelle architecture de DNN, STGCN, pour la classification de gestes associés à des pathologies telles que l'arthrose du genou et la maladie de Parkinson à partir d'analyses vidéo de la marche, ces travaux offrent de nouvelles perspectives pour l'amélioration du diagnostic et de la prise en charge médicale grâce aux progrès dans le domaine de l'IA
This 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
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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.

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Esling, Philippe. "Multiobjective time series matching and classification." Paris 6, 2012. http://www.theses.fr/2012PA066704.

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Plusieurs millions d’années d’évolution génétique ont façonné notre système auditif, permettant d’effectuer une discrimination flexible des événements acoustiques. Nous pouvons ainsi traiter simultanément plusieurs échelles de perception contradictoires de manière multidimensionnelle. De plus, nous avons une capacité à extraire une structure cohérente à partir de formes temporelles. Nous montrons qu’en émulant ces mécanismes dans nos choix algorithmiques, nous pouvons créer des approches efficaces de recherche et classification, dépassant le cadre des problématiques musicales. Nous introduisons ainsi le problème de MultiObjective Time Series (MOTS) et proposons un algorithme efficace pour le résoudre. Nous introduisons deux paradigmes innovants de recherche sur les fichiers audio. Nous introduisons un nouveau paradigme de classification basé sur la domination d'hypervolume, appelé HyperVolume-MOTS (HV-MOTS). Ce système étudie le comportement de la classe entière par sa distribution et sa diffusion sur l’espace d’optimisation. Nous montrons une amélioration sur les méthodes de l’état de l’art sur un large éventail de problèmes scientifiques. Nous présentons ainsi un système d’identification biométrique basée sur les sons produit par les battements de coeur, atteignant des taux d’erreur équivalents à d’autres caractéristiques biométriques. Ces résultats sont confirmés par le l'ensemble de données cardiaques de l’étude d’isolation Mars500. Enfin, nous étudions le problème de la génération de mélanges sonores orchestraux imitant au mieux une cible audio donnée. L'algorithme de recherche basé sur le problème MOTS nous permet d’obtenir un ensemble de solutions efficaces
Millions 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
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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.

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Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering
The 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.
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Lines, Jason. "Time Series classification through transformation and ensembles." Thesis, University of East Anglia, 2015. https://ueaeprints.uea.ac.uk/53360/.

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The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, offers a specific challenge. Unlike traditional classification problems, the ordering of attributes is often crucial for identifying discriminatory features between classes. TSC problems arise across a diverse range of domains, and this variety has meant that no single approach outperforms all others. The general consensus is that the benchmark for TSC is nearest neighbour (NN) classifiers using Euclidean distance or Dynamic Time Warping (DTW). Though conceptually simple, many have reported that NN classifiers are very difficult to beat and new work is often compared to NN classifiers. The majority of approaches have focused on classification in the time domain, typically proposing alternative elastic similarity measures for NN classification. Other work has investigated more specialised approaches, such as building support vector machines on variable intervals and creating tree-based ensembles with summary measures. We wish to answer a specific research question: given a new TSC problem without any prior, specialised knowledge, what is the best way to approach the problem? Our thesis is that the best methodology is to first transform data into alternative representations where discriminatory features are more easily detected, and then build ensemble classifiers on each representation. In support of our thesis, we propose an elastic ensemble classifier that we believe is the first ever to significantly outperform DTW on the widely used UCR datasets. Next, we propose the shapelet-transform, a new data transformation that allows complex classifiers to be coupled with shapelets, which outperforms the original algorithm and is competitive with DTW. Finally, we combine these two works with with heterogeneous ensembles built on autocorrelation and spectral-transformed data to propose a collective of transformation-based ensembles (COTE). The results of COTE are, we believe, the best ever published on the UCR datasets.
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Ghalwash, Mohamed. "Interpretable Early Classification of Multivariate Time Series." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/239730.

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Анотація:
Computer and Information Science
Ph.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
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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.

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Botsch, Michael-Felix. "Machine learning techniques for time series classification." Göttingen Cuvillier, 2009. http://d-nb.info/994721455/04.

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Книги з теми "Classification of biomedical time series"

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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.

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2

Abel, 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.

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3

Time Series Clustering and Classification. Chapman and Hall/CRC, 2019.

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4

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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5

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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7

Maharaj, Elizabeth Ann, Pierpaolo D'Urso, and Jorge Caiado. Time Series Clustering and Classification. Taylor & Francis Group, 2019.

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8

Maharaj, Elizabeth Ann, Jorge Caiado, and Pierpaolo DUrso. Time Series Clustering and Classification. Taylor & Francis Group, 2021.

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9

Buza, Krisztian. Fusion Methods for Time-Series Classification. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2011.

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Volna, Eva, Martin Kotyrba, and Michal Janosek. Pattern Recognition and Classification in Time Series Data. IGI Global, 2016.

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Частини книг з теми "Classification of biomedical time series"

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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.

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Bock, 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.

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Abid, 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.

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Acerbi, 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.

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De, 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.

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Grabocka, 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.

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Kotsifakos, 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.

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Roychoudhury, 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.

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Camiz, 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.

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Hong, 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.

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Тези доповідей конференцій з теми "Classification of biomedical time series"

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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.

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Casella, 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.

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Fong, 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.

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Matarmaa, 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.

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Wang, 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.

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Mure, 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.

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Mbouopda, 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.

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Анотація:
Time series analysis has gained a lot of interest during the last decade with diverse applications in a large range of domains such as medicine, physic, and industry. The field of time series classification has been particularly active recently with the development of more and more efficient methods. However, the existing methods assume that the input time series is free of uncertainty. However, there are applications in which uncertainty is so important that it can not be neglected. This project aims to build efficient, robust, and interpretable classification methods for uncertain time series.
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Krawczak, 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.

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Li, 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.

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Hsieh, 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.

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Звіти організацій з теми "Classification of biomedical time series"

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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.

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Sinkovits, 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.

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Schryver, 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.

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Danvers, 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.

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Senin, 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.

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Lasko, 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.

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Анотація:
A near-global framework for automated training data generation and land cover classification using shallow machine learning with low-density time series imagery does not exist. This study presents a methodology to map nine-class, six-class, and five-class land cover using two dates of a Sentinel-2 granule across seven international sites. The approach uses a series of spectral, textural, and distance decision functions combined with modified ancillary layers to create binary masks from which to generate a balanced set of training data applied to a random forest classifier. For the land cover masks, stepwise threshold adjustments were applied to reflectance, spectral index values, and Euclidean distance layers, with 62 combinations evaluated. Global and regional adaptive thresholds were computed. An annual 95th and 5th percentile NDVI composite was used to provide temporal corrections to the decision functions, and these corrections were compared against the original model. The accuracy assessment found that the regional adaptive thresholds for both the two-date land cover and the temporally corrected land cover could accurately map land cover type within nine-class, six-class, and five-class schemes. Lastly, the five-class and six-class models were compared with a manually labeled deep learning model (Esri), where they performed with similar accuracies. The results highlight performance in line with an intensive deep learning approach, and reasonably accurate models created without a full annual time series of imagery.
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Arévalo-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.

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Background: It is well known that edema can persist after meningioma resection, and sometimes it is not resolved after this time. This study aimed to establish the relationship between a series of variables associated with meningioma or surgery, and the duration of postoperative edema. Methods: We conducted a retrospective study of 77 meningiomas resected at our institution between January 2016 and January 2018 with a maximum follow-up period of up to three years. The independent variables collected were demographics, tumor location, relationship with the sinuses (invasion/contact), relationship with arterial structures, deviation from the midline, volume (cm3), degree of initial edema, WHO histological classification, degree of atypia, degree of resection, previous embolization, and development of complications. The edema levels were classified according to the classification described by Ide et al. (1995): GR0, GR1, and GR2. Measurements were performed using FLAIR magnetic resonance sequences. Statistical analyses were performed using the SPSS 21. Results: Age (p=0.003), deviation from the midline (p=0.001), and tumor volume (p<0.001) were correlated with outcome using Spearman's test. Univariate analysis revealed that the localization (p=0.016), initial edema (p<0.001), degree of atypia (p=0.019), and presence of previous embolization (p=0.037) were statistically significant. In multivariate analysis, only age, initial edema, and embolization were significant independent predictors. Conclusion: These results suggest that the degree of initial edema, midline deviation, tumor volume, tumor location, degree of atypia, and previous embolization may be important predictors of postoperative edema duration.
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Berney, 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.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.
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Research, IFF. FSA and Official Controls: Research with Food Business Operators. Food Standards Agency, February 2023. http://dx.doi.org/10.46756/sci.fsa.drn484.

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The Food Standards Agency (FSA) is an independent Government body, established in 2000 to protect public health and consumer interests in relation to food. The FSA is the Central Competent Authority (CCA) responsible for the delivery of official food and feed controls in England, Northern Ireland and Wales.. In Northern Ireland, officials from the Department of Agriculture, Environment and Rural Affairs (DAERA) carry out meat hygiene official controls in approved establishments on behalf of the FSA. Food Business Operators (FBOs) in the dairy, meat and wine sectors have a direct relationship with the Food Standards Agency (FSA) via its Official Controls, including inspections, enforcement, advice and guidance. The FSA and local authorities work together deliver shellfish controls. The FSA is responsible for conducting sanitary surveys and awarding the classification status of production and relaying areas. Some FBOs in the shellfish sector have a direct relationship with the FSA in relation to its functions however local authorities are the primary point of contact for the majority. This research study – collecting the views of FBOs themselves – was intended to support the rollout of the OTP programme, and the implementation of Official Controls. The study entailed a quantitative survey of 400 FBOs based in England, Wales and Northern Ireland, followed by in-depth interviews with 60 FBOs. Fieldwork took place between June and August 2022. Questionnaire coverage included FBOs’ experience of working with the FSA, their understanding of what the FSA does, the impacts of the coronavirus (COVID-19) pandemic, the UK’s exit from the European Union (EU), and their familiarity with the OTP. The methodology adopted a similar approach to the first wave of the research, conducted in 2020, to enable time series analysis. However, this 2022 wave of the research has expanded to include the views of FBOs in Northern Ireland and those in the shellfish sector.
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