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dc.contributor.author
Bock, Christian
dc.contributor.author
Moor, Michael
dc.contributor.author
Jutzeler, Catherine R.
dc.contributor.author
Borgwardt, Karsten
dc.contributor.editor
Cartwright, Hugh
dc.date.accessioned
2021-01-21T14:18:35Z
dc.date.available
2021-01-13T13:36:30Z
dc.date.available
2021-01-21T14:18:35Z
dc.date.issued
2021
dc.identifier.isbn
978-1-0716-0825-8
en_US
dc.identifier.isbn
978-1-0716-0826-5
en_US
dc.identifier.issn
1064-3745
dc.identifier.issn
1940-6029
dc.identifier.other
10.1007/978-1-0716-0826-5_2
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/462139
dc.description.abstract
With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learning methods that allow its mining and exploitation. Classification is one of the most important and challenging machine learning tasks related to time series. Many biomedical phenomena, such as the brain’s activity or blood pressure, change over time. The objective of this chapter is to provide a gentle introduction to time series classification. In the first part we describe the characteristics of time series data and challenges in its analysis. The second part provides an overview of common machine learning methods used for time series classification. A real-world use case, the early recognition of sepsis, demonstrates the applicability of the methods discussed.
en_US
dc.language.iso
en
en_US
dc.publisher
Humana
en_US
dc.subject
Time series
en_US
dc.subject
Classification
en_US
dc.subject
Onset detection
en_US
dc.subject
Subsequence mining
en_US
dc.subject
Deep learning
en_US
dc.title
Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning
en_US
dc.type
Book Chapter
dc.date.published
2020-08-18
ethz.book.title
Artificial Neural Networks
en_US
ethz.journal.title
Methods in Molecular Biology
ethz.journal.volume
2190
en_US
ethz.journal.abbreviated
Methods Mol Biol
ethz.pages.start
33
en_US
ethz.pages.end
71
en_US
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09486 - Borgwardt, Karsten M. / Borgwardt, Karsten M.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09486 - Borgwardt, Karsten M. / Borgwardt, Karsten M.
en_US
ethz.date.deposited
2021-01-13T13:36:38Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-01-21T14:18:43Z
ethz.rosetta.lastUpdated
2021-01-21T14:18:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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