Monitoring of Cardiac Arrhythmia Patterns by Adaptive Analysis

Herrera, Jose E.; Jorge, Ricardo Rodríguez; Villegas, Osslan Osiris Vergara; Sánchez, Vianey Guadalupe Cruz; Bíla, Jiří; Nandayapa, Manuel; Ponce, Israel U.; Marrufo, Ángel Israel Soto; Abad, Angel Flores

doi:10.1007/978-3-319-49109-7_86

Cited by 2 publications

(2 citation statements)

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“…to present. [9][10][11] The data mining analysis tool KEEL provides various functions ranging from data preprocessing to analysis. KEEL project team shares KEEL-dataset repository so that their research results can be compared with other studies in the future.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, there are organizations that provide a large amount of standard data to support analytical research. MIT‐BIH Arrhythmia Database 8 is the first set of standard test data for evaluating arrhythmia detectors and has been used to verify various analytical methods form 1980s to present 9–11 . The data mining analysis tool KEEL provides various functions ranging from data preprocessing to analysis.…”

Section: Related Workmentioning

confidence: 99%

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Dataset retrieval system based on automation of data preparation with dataset description model

Mun

Lee

Choi

et al. 2019

Concurrency and Computation

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Data preparation is the most effortful task in the process of statistical learning. Many studies related to data mining are performed without data preparation by assuming that qualified datasets are already prepared. It may hide useful patterns of data, which can result in poor performance and incorrect learning. Automation of data preparation can solve these problems. For automation of data preparation, a few issues should be considered, such as flexible expression of requirements according to the purpose of the learning model, accessibility to data sources, and performance degradation due to automation. In this paper, we propose a dataset description model that can express the requirements for data processing and dataset retrieval system based on automated data preparation. The proposed system makes it possible to provide good quality datasets for statistical learning applications using data preparation methods such as data acquisition, refinement, and organization. In the experiment, we demonstrate that the proposed system doesn't have performance loss as compared to the existing manual systems. Moreover, the quality of the datasets are also improved by using the proposed system.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%