Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning
Metadata only
Datum
2021Typ
- Book Chapter
ETH Bibliographie
yes
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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. Mehr anzeigen
Publikationsstatus
publishedHerausgeber(in)
Buchtitel
Artificial Neural NetworksZeitschrift / Serie
Methods in Molecular BiologyBand
Seiten / Artikelnummer
Verlag
HumanaThema
Time series; Classification; Onset detection; Subsequence mining; Deep learningOrganisationseinheit
09486 - Borgwardt, Karsten M. (ehemalig) / Borgwardt, Karsten M. (former)
09769 - Jutzeler, Catherine / Jutzeler, Catherine
ETH Bibliographie
yes
Altmetrics