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Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning.

Christian Bock^1,2, Michael Moor^1,2, Catherine R Jutzeler^1,2

¹Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.

Methods in Molecular Biology (Clifton, N.J.)

|August 18, 2020

Summary

This summary is machine-generated.

This chapter introduces time series classification for biomedical data analysis. It covers data characteristics, machine learning methods, and a sepsis recognition case study.

Keywords:

Classification Deep learning Onset detection Subsequence mining Time series

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Area of Science:

Biomedical data analysis
Machine learning
Time series analysis

Background:

The biomedical field generates vast amounts of time series data.
Analyzing this data requires specialized machine learning (ML) techniques.
Time series classification is crucial for understanding dynamic biomedical phenomena.

Purpose of the Study:

To provide an introductory overview of time series classification.
To discuss the unique characteristics and challenges of biomedical time series data.
To demonstrate the practical application of ML methods in this domain.

Main Methods:

Exploration of time series data properties.
Review of common machine learning algorithms for classification.
Application of methods to a real-world biomedical problem.

Main Results:

Detailed explanation of time series data challenges.
Overview of various machine learning approaches for classification.
Successful demonstration of early sepsis recognition using discussed methods.

Conclusions:

Time series classification is a vital tool for biomedical data mining.
Machine learning offers powerful methods for analyzing dynamic biomedical data.
Early sepsis recognition exemplifies the clinical utility of these techniques.