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Relevant bibliographies by topics / Classification of biomedical time series

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Journal articles

Academic literature on the topic 'Classification of biomedical time series'

Author: Grafiati

Published: 21 December 2024

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Journal articles on the topic "Classification of biomedical time series"

1

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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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 experimenta

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3

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 (2021): 2398–408. http://dx.doi.org/10.1109/jbhi.2021.3060997.

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4

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 (2013): 634–44. http://dx.doi.org/10.1016/j.bspc.2013.06.004.

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5

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 (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

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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 (2009): 32–37. http://dx.doi.org/10.1109/memb.2009.932903.

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7

Wang, Will Ke, Ina Chen, Leeor Hershkovich, et al. "A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications." Sensors 22, no. 20 (2022): 8016. http://dx.doi.org/10.3390/s22208016.

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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 t

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8

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 (2019): 49. http://dx.doi.org/10.3390/e22010049.

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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

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9

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 com

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10

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 (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 expressio

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